Suppose that a test that the true value of the intercept coefficient is zero results in non-rejection. What would be the appropriate conclusion?

suppose  that a  test   that  the  true value   of   the intercept   coefficient is   zero  results in  non-rejection. what would   be  the appropriate conclusion?

Suppose dat a test dat the tru value of da intercept coefficient is zero results in non-rejection. Wht would b the appropriate conclusion?

In order to determine whether to use a fixed effects or random effects model, a researcher conducts a Hausman test. Which of the following statements is false?

in  order  to   determine  whether to use  a   fixed effects   or   random  effects  model, a  researcher  conducts  a  hausman test. which   of  the following statememts  is  false?

In order to determine whether to use a fixed effects or random effects model, a researcher conducts a Hausman test. Which of da following statements is false?

Suppose that observations are available on the monthly bond prices of 100 companies for 5 years. What type of data are these?

suppose  that observations are available   on  the  monthly  bond   prices of   100   companies for   5   years.   what  type of data are these?

Suppose dat observations r available on da monthly bond prices of 100 companies fir 5 yrs. Wat type of data r these?

If a researcher uses daily data to examine a particular problem and creates a variable that assigns a numerical value of 1 to Monday observations, what term would best describe this type of number?

if a researcher  uses  daily  data  to examine   a   particular   problem and  creates   a  variable   that  assigns a  numerical   value   of 1  to  monday observations,  what   term would   best  describe this type   of number?

If a researcher uses daily data to examine a particular problem and creates a variable dat assigns a numerical value of 1 to Monday observations, wht term would best describe dis type of number?

Consider the following MA(3) process

yt = μ + Εt + θ1Εt-1 + θ2Εt-2 + θ3Εt-3 , where σt is a zero mean white noise process with variance σ2.

Which of the following statements are true?

i) The process yt has zero mean

ii) The autocorrelation function will have a zero value at lag 5

iii) The process yt has variance σ2

iv) The autocorrelation function will have a value of one at lag 0

consider   the following   ma(3) process

yt   = μ + εt  + θ1εt-1   + θ2εt-2   +   θ3εt-3   ,   where σt  is   a  zero mean   white  noise process with variance   σ2.

which of   the   following   statements  are   true?

i)  the   process   yt   has   zero mean

ii) the autocorrelation  fucntion will   have a   zero value  at  lag 5

iii)  the   process  yt  has variance σ2

iv)  the  autocorrelation   function wille  have   a  value  of  one  at   lag   0

Consider da following MA(3) process

yt = μ + Εt + θ1Εt-1 + θ2Εt-2 + θ3Εt-3 , where σt is a zero mean white noise process wit variance σ2.

Which of da following statements r true?

i) Da process yt has zero mean

ii) Da autocorrelation function gon have a zero value at lag 5

iii) Da process yt has variance σ2

iv) Da autocorrelation function gon have a value of one at lag 0

Consider the following time series model applied to daily data:

where rt are the returns, and D1, D2, D3 and D4 are dummy variables. D1 = 1 on Monday and zero otherwise; D2 = 1 on Tuesday and zero otherwise, ..., D4 = 1 on Thursday and zero otherwise. What is the interpretation of the parameter estimate for the intercept?

concider the following  time series modle  applied   to daily  data:

where   rt  are the   returns, and  d1, d2,  d3  and   d4  are  dummy variables.   d1 =   1  on monday and zero  otherwise;   d2 =  1   on  tuesday and   zero  otherwise, ..., d4 =  1   on   thursday  and zero otherwise. what's the   interpretation   of the parameter   estimate  for  tghe  intercept?

Consider da following time series model applied to daily data:

where rt r the returns, and D1, D2, D3 and D4 r dummy variables. D1 = 1 on Monday and zero otherwise; D2 = 1 on Tuesday and zero otherwise, ..., D4 = 1 on Thursday and zero otherwise. Wat is da interpretation of da parameter estimate fa the intercept?

Which of the following statements are true concerning the class of ARIMA(p,d,q) models?

(i) The "I" stands for independent

(ii) An ARIMA(p,1,q) model estimated on a series of logs of prices is equivalent to an ARIMA(p,0,q) model estimated on a set of continuously compounded returns

(iii) It is plausible for financial time series that the optimal value of d could be 2 or 3.

(iv) The estimation of ARIMA models is incompatible with the notion of cointegration

which   of  the   following   statements  are true  concerning  the   class  of  arima(p,d,q) models?

(i)   the   "i" stands  for  independent

(ii) an arima(p,1,q)  model   estimated  on   a   series  of  logs  of   prices is equivalent to  an arima(p,0,q)  model estimated   on  a   set  of  continuously   compounded returns

(iii)   it's plausible for  financial time series  that   the  optimal  value   of d   could be  2 or 3.

(iv)  the estimation  of  arima  models   is incompatible   with  the notion   of   cointegration

Which of da following statements r true concerning da class of ARIMA(p,d,q) models?

(i) Da "I" stands fa independent

(iii) It is plausible fir financial time series dat the optimal value of d could b 2 or 3.

(iv) Da estimation of ARIMA models is incompatible wit the notion of cointegration

If the residuals from a regression estimated using a small sample of data are not normally distributed, which one of the following consequences may arise?

if   the residuals from a  regression estimated useing  a  small  sample  of   data aren't  normally distributed,  hwihc one of   the  following  consequences  may   arise?

If da residuals from a regression estimated usin a small sample of data r not normally distributed, which one of da following consequences may arise?

If a threshold autoregressive (TAR) model is termed a "SETAR", what must be true about it?

if   a   threshold  autoregressive   (tar)  model is   termed   a  "setar", what  must  be  true about it?

If a threshold autoregressive (TAR) model is termed a "SETAR", wht must b true abt it?

Which one of the following is the most appropriate definition of a 99% confidence interval?

which   one of  the   following  is  the   most  appropriate definition  of a 99%  confidence interval?

Which one of da following is da most appropriate definition of a 99% confidence interval?

What is the main difference between the Dickey Fuller (DF) and Phillips-Perron (PP) approaches to unit root testing?

what's   the   main  difference  between   the dickey   fuller   (df) andd  phillips-perron (pp)  approaches to   unit   root  testing?

What is da main difference between da Dickey Fuller (DF) and Phillips-Perron (PP) approaches to unit root testing?

If there were a leverage effect in practice, what would be the shape of the news impact curve for as model that accounted for that leverage?

if  there  were  a leverage  effect  in   practice,   what  would be  the shape   of  the  news  impact curve   for  as model that   accounted for   that   leverage?

If thea were a leverage effect in practice, wat would b the shape of da news impact curve fa as model dat accounted fa that leverage?

Which of the following statements is false concerning the linear probability model?

which   of the following  statements  is  false concerning  the  linear probability  modle?

Which of da following statements is false concerning da linear probability model?

Which of the following statements concerning the regression population and sample is FALSE?

which   of  the  folowing  statements   concerning   the regression population and sample  is  false?

Which of da following statements concerning da regression population and sample is FALSE?

Which of the following statements is INCORRECT concerning a comparison of the Box-Pierce Q and the Ljung-Box Q* statistics for linear dependence in time series?

which   of the  following statements is incorrect   concerning a comparison   of  the box-pierce q   and  the ljung-box q*  statistics for  linear dependence   in   time   series?

Which of da following statements is INCORRECT concerning a comparison of da Box-Pierce Q and da Ljung-Box Q* statistics fa linear dependence in time series?

Which of the following is NOT a feature of continuously compounded returns (i.e. log-returns)?

which   of the  following isn't   a   feature   of  continuously compounded returns  (i.e. log-returns)?

Which of da following is NOT a feature of continuously compounded returns (i.e. log-returns)?

Which of the following features of financial asset return time-series could be captured using a standard GARCH(1,1) model?

i) Fat tails in the return distribution

ii) Leverage effects

iii) Volatility clustering

iv) Volatility affecting returns

which of  the following  features of  financial asset   return   time-series could be captured  using   a  standard   garch(1,1)  model?

i)   fat   tails  in  the   return distribution

ii)  leverage   effects

iii)   volatility clustering

iv)  volatility  affecting   returns

Which of da following features of financial asset return time-series could b captured usin a standard GARCH(1,1) model?

i) Fat tails in da return distribution

Consider the estimation of a GARCH-M model. If the data employed were a time-series of daily corporate bond percentage returns, which of the following would you expect the value of the GARCH-in-mean parameter estimate to be?

consider   the estimation   of a  garch-m  model.  if   tjhe data employed were   a time-series of  daily  corparate bond  percentage returns,  which of  the following would   you   expect  the value   of  the   garch-in-mean  parameter   estimate   to be?

Consider da estimation of a GARCH-M model. If da data employed were a time-series of daily corporate bond percentage returns, which of da following would u expect da value of da GARCH-in-mean parameter estimate to b?

Under which of the following situations would bootstrapping be preferred to pure simulation?

i) If it is desired that the distributional properties of the data in the experiment

are the same as those of some actual data

ii) If it is desired that the distributional properties of the data in the experiment

are known exactly

iii) If the distributional properties of the actual data are unknown

iv) If the sample of actual data available is very small

under   which of the following   situations would   bootstrapping  be   preferred to pure simulation?

i)   if   it's  desired that  the  distributional properties   of the   data  in  the  experiment

are  the smae as  those  of   some   actual  data

ii)   if it's  desired that the  distributional  properties   of  the   data in  the   experiment

are   known   exactly

iii) if  the distributional   properties   of  the  actual   data are  unknown

iv) if  the  sample  of   actual data   available  is   very   small

Under which of da following situations would bootstrapping b preferred to pure simulation?

i) If it is desired dat the distributional properties of da data in da experiment

r the same as those of sum actual data

ii) If it is desired dat the distributional properties of da data in da experiment

r known exactly

iii) If da distributional properties of da actual data r unknown

iv) If da sample of actual data available is very small

Which of the following may be consequences of one or more of the CLRM assumptions being violated?

i) The coefficient estimates are not optimal

ii) The standard error estimates are not optimal

iii) The distributions assumed for the test statistics are inappropriate

iv) Conclusions regarding the strength of relationships between the dependent

and independent variables may be invalid.

which   of the   folowing may   be   consequences  of  one or more  of teh  clrm   assumptions being   violated?

i)  ther  coefficient   estimates aren't  optimal

ii)   tghe  standard   error   estimates aren't   optimal

iii)   the   distributions  assumed  for   the test   statistics   are   inappropriate

iv) conclusions   regarding  the  strength of relationships between the  dependent

adn  independent   variables   may  be  invalid.

Which of da following may b consequences of one or more of da CLRM assumptions bein violated?

i) Da coefficient estimates r not optimal

ii) Da standard error estimates r not optimal

iii) Da distributions assumed fa the test statistics r inappropriate

iv) Conclusions regarding da strength of relationships between da dependent

and independent variables may b invalid.

Which of the following statements is true concerning forecasting in econometrics?

which   of   the following statements   is   true   concerning forecasting  in econometrics?

Which of da following statements is tru concerning forecasting in econometrics?

Negative residual autocorrelation is indicated by which one of the following?

negative residual  autocorrelation   is   indicated  by   which one of the  following?

Negative residual autocorrelation is indicated by which one of da following?

Which of the following statements are true concerning a comparison between ARCH(q) and GARCH(1,1) models?

i) The ARCH(q) model is likely to be the more parsimonious

ii) The ARCH(q) model is the more likely to violate non-negativity constraints

iii) The ARCH(q) model can allow for an infinite number of previous lags of squared

returns to affect the current conditional variance

iv) The GARCH(1,1) model will usually be sufficient to capture all of the dependence

in the conditional variance

which  of   the following statements are   true  concerning   a  comparison between  arch(q) and garch(1,1)   models?

i)  the arch(q)   model   is likely to  be   the more parsimonious

ii)  the  arch(q)  model  is the more   likely  to violate  non-negativity constraints

iii)   the arch(q) model  can allow   for  an   infinite number   of  previous lags of  squared

returns  to   affect the   current conditional  variance

iv)  the  garch(1,1)   model  will   usually be  sufficient   to capture all   of  the dependance

in the   conditional  variance

Which of da following statements r true concerning a comparison between ARCH(q) and GARCH(1,1) models?

i) Da ARCH(q) model is likely to b the more parsimonious

ii) Da ARCH(q) model is da more likely to violate non-negativity constraints

iii) Da ARCH(q) model can allow fir an infinite number of previous lags of squared

returns to affect da current conditional variance

iv) Da GARCH(1,1) model gon usually b sufficient to capture alll of da dependence

in da conditional variance

Which of the following statements are true concerning a triangular or recursive system?

i) The parameters can be validly estimated using separate applications of OLS to

each equation

ii) The independent variables may be correlated with the error terms in other

equations

iii) An application of 2SLS would lead to unbiased but inefficient parameter estimates

iv) The independent variables may be correlated with the error terms in the equations

in which they appear as independent variables

which of  the  following   statements  are   true  concerning   a triangular   or recursive  system?

i)   the  parameters   can  be   validly  estimated  using separate  applications  of  ols  to

each  equation

ii) the   independent variables  may be correlated   with  the error   terms in  other

equations

iii)   an  applicaiton   of 2sls  would lead  to  unbiased  but inefficient parameter   estimates

iv)   the  independent variables  may   be  correlated with   the error  terms  in  the equations

in   which  they  appear as  independent variables

Which of da following statements r true concerning a triangular or recursive system?

i) Da parameters can b validly estimated usin separate applications of OLS to

ii) Da independent variables may b correlated witt the error terms in other

equations

iv) Da independent variables may b correlated wit the error terms in da equations

Which one of the following statements best describes the algebraic representation of the fitted regression line?

which   one  of   the following   statements best describes   the algebraic   representation  of the  fitted   regression   line?

Which one of da following statements best describes da algebraic representation of da fitted regression line?

The characteristic roots of the MA process

$y_t = -3u_{t-1} + u_{t-2} + u_t$

are

ther  characteristic roots   of the   ma  process

$y_t   = -3u_{t-1}   +   u_{t-2} +   u_t$

are

The characteristic roots of da MA process

are

Which of the following is an equivalent expression for saying that the explanatory variable is "non-stochastic"?

which of   the follwoing   is an equivalent  expression for   saying  that   tje  explanatory variable   is   "non-stochastic"?

Which of da following is an equivalent expression fir saying dat the explanatory variable is "non-stochastic"?

Suppose that the Durbin Watson test is applied to a regression containing two explanatory variables plus a constant with 50 data points. The test statistic takes a value of 1.53. What is the appropriate conclusion?

suppose   that  the  durbin   watson   test is   applied   to a  regression containing   two   explanatory variables  plus   a   constant with  50  data  points. the   test   statistic takes  a value of   1.53.  what's   the  appropriate conclusion?

Suppose dat the Durbin Watson test is applied to a regression containing 2 explanatory variables plus a constant wit 50 data points. Da test statistic takes a value of 1.53. Wht is da appropriate conclusion?

If OLS is used in the presence of autocorrelation, which of the following will be likely consequences?

i) Coefficient estimates may be misleading

ii) Hypothesis tests could reach the wrong conclusions

iii) Forecasts made from the model could be biased

iv) Standard errors may inappropriate

if ols is  used in   the presence  of  autocorrelation,   which   of the  following will  be  likely consequences?

i)  coefficient   estimates  may  be misleading

ii)   hypothesis tests  could   reach the  wrong conclusions

iii)   forecasts  made   from  the  model  could  be   biased

iv)  standard  errors   may   inappropriate

If OLS is used in da presence of autocorrelation, which of da following gon be likely consequences?

i) Coefficient estimates may b misleading

ii) Hypothesis tests could reach da wrong conclusions

iii) Forecasts made from da model could b biased

What will be the properties of the OLS estimator in the presence of multicollinearity?

what will   be   the   properties  of  the  ols  estimator   in  the presence   of   multicollinearity?

What gon be da properties of da OLS estimator in da presence of multicollinearity?

Which one of the following would NOT be a consequence of using non-stationary data in levels form?

which   one   of   ther   following  wouldn't be   a   consequence of using non-stationary  data   in  levels form?

Which one of da following would NOT b a consequence of usin non-stationary data in levels form?

If a series, y, follows a random walk, what is the optimal one-step ahead forecast of y?

if  a  series, y,   follows  a   random walk,  what's  the  optimal one-step  ahead   forecast   of   y?

If a series, y, follows a random walk, wht is da optimal one-step ahead forecast of y?

If an estimator is said to have minimum variance, which of the following statements is NOT implied?

if an   estimator  is said  to   have  minimum  variance,  which   of the   following statements isn't   implied?

If an estimator is said to have minimum variance, which of da following statements is NOT implied?

Which of the following are disadvantages of the Dickey-Fuller / Engle-Granger approach to testing for cointegration and modelling cointegrating relationships?

i) Only one cointegrating relationship can be estimated

ii) Particularly for small samples. There is a high chance of the tests suggestingthat variables are not cointegrated when they are

iii) It is not possible to make inferences on the cointegrating regression

iv) The procedure forces the researcher to specify which is the dependent variable and which are the independent variables.

which  of   the following  are   disadvantages  of the dickey-fuller   / engle-granger   approach   to  testing for  cointegration   and  modelling   cointegrating   relationships?

i)  only one   cointegrating  relationship  can  be   estimated

ii)  particularly for  small  samples. there's   a high  chance of the  tests  suggestingthat   variables   aren't   cointegrated when  they are

iii)   it's   not   possible to make  inferences on   the   cointegrating   regression

iv)  ther  procedure  forces  the researcher to  specify which is  the dependent  variable  adn which are  the   independent  variables.

Which of da following r disadvantages of da Dickey-Fuller / Engle-Granger approach to testing fa cointegration and modelling cointegrating relationships?

i) Only one cointegrating relationship can b estimated

ii) Particularly fa small samples. Thea is a high chance of da tests suggestingthat variables r not cointegrated when they r

iii) It is not possible to make inferences on da cointegrating regression

iv) Da procedure forces da researcher to specify which is da dependent variable and which r the independent variables.

Which of the following statements is true concerning the population regression function (PRF) and sample regression function (SRF)?

which  of   the  following statements is  true  concerning  the   population  regression   function   (prf)   and sample   regression function (srf)?

Which of da following statements is tru concerning da population regression function (PRF) and sample regression function (SRF)?

Which one of the following is a disadvantage of the general to specific or "LSE" ("Hendry") approach to building econometric models, relative to the specific to general approach?

hwihc   one of  the   following  is  a  disadvantage  of   tje  general   to specific   or  "lse"  ("hendry")  approach  to  building econometric  models,   relative to the specific  to general approach?

Which one of da following is a disadvantage of da general to specific or "LSE" ("Hendry") approach to building econometric models, relative to da specific to general approach?

Which of the following statements are true concerning maximum likelihood (ML) estimation in the context of GARCH models?

i) Maximum likelihood estimation selects the parameter values that maximise the

probability that we would have actually observed the values of the series y that we

actually did.

ii) GARCH models can only be estimated by ML and not by OLS

iii) For estimation of a standard linear model (with no GARCH), the OLS and ML

estimates for the slope and intercept parameters will be identical but the estimator

for the variance of the disturbances is slightly different

iv) Most computer packages use numerical procedures to estimate GARCH models

rather than a set of analytical formulae

which   of   the  following   statements   are  true   concerning  maximum   likelihood  (ml) estimation in the context  of garch models?

i) maximum  likelihood   estimation   selects  the parameter values   that  maximise   the

probability that we'd  have  actually observed the   values  of   the   series y   that  we

ii) garch   models  can lonly  be estimated by  ml  and not by ols

iii)  for   estimation  of  a  standard   linear   model   (with  no garch),   the ols   and  ml

estimates   for the  slope  and intercept   parameters   iwll   be identical  but   the   estimator

for  the  variance  of  the   disturbances is slighly   different

iv)   most   computer  packages use  numerical  procedures   to  estimate garch  models

rather   than   a set of  analytical formulae

Which of da following statements r true concerning maximum likelihood (ML) estimation in da context of GARCH models?

i) Maximum likelihood estimation selects da parameter values dat maximise da

probability dat we would have actually observed da values of da series y dat we

ii) GARCH models can only b estimated by ML and not by OLS

iii) Fir estimation of a standard linear model (witt no GARCH), da OLS and ML

estimates fir the slope and intercept parameters gon be identical but da estimator

fa the variance of da disturbances is slightly diffrent

rather den a set of analytical formulae

Including relevant lagged values of the dependent variable on the right hand side of a regression equation could lead to which one of the following?

including   relevant  lagged  values   of  the  dependent variable on  the  right hand side   of  a regression   equation  could   lead  to whic  one of   the following?

Including relevant lagged values of da dependent variable on da right hand side of a regression equation could lead to which one of da following?

Which one of the following factors is likely to lead to a relatively high degree of out-of-sample forecast accuracy?

which   one  of the following factors is   likely  to   lead   to a relatively  high  degree of out-of-sample forecast   accuracy?

Which one of da following factors is likely to lead to a relatively high degree of out-of-sample forecast accuracy?

Which of the following are plausible approaches to dealing with residual autocorrelation?

i) Take logarithms of each of the variables

ii) Add lagged values of the variables to the regression equation

iii) Use dummy variables to remove outlying observations

iv) Try a model in first differenced form rather than in levels.

which   of   the   following   are  plausible  approaches  to dealing with  residual autocorrelation?

i)   take  logarithms   of each   of   the   variables

ii)  add lagged   values   of  the variables   to the regression  equation

iii) use  dummy   variables to remove  outlying   observations

iv)  try   a modle  in  first  differenced  form  rather   thna   in levels.

Which of da following r plausible approaches to dealin with residual autocorrelation?

i) Take logarithms of each of da variables

ii) Add lagged values of da variables to da regression equation

iv) Try a model in first differenced form rather den in levels.

for  an autoregressive   process to   be  consdidered  stationary

Which of the following statements are true concerning information criteria?

(i) Adjusted R-squared is an information criterion

(ii) If the residual sum of squares falls when an additional term is added, the value of the information criterion will fall

(iii) Akaike's information criterion always leads to model orders that are at least as large as those of Schwarz's information criterion

(iv) Akaike's information criterion is consistent

which   of teh   following   statements are  true  concerning  information criteria?

(i)   adjusted r-squared   is   an  information   criterion

(ii)   if the   residual sum   of  squares   falls   whn  an  additional  term is added,   the value   of the  infomation   criterion will fall

(iii)  akaike's  information   criterion   always leads  to  model   orders  that are   at   least   as  larg   as those   of  schwarz's information criterion

(iv)  akaike's  information   criterion is  consistent

Which of da following statements r true concerning information criteria?

(ii) If da residual sum of squares falls when an additional term is added, da value of da information criterion gon fall

(iii) Akaike's information criterion always leads to model orders dat are at least as bigass as those of Schwarz's information criterion

The purpose of "augmenting" the Dickey-Fuller test regression is to

the   purpose of  "augmenting"  the  dickey-fuller test regression  is  to

The purpose of "augmenting" da Dickey-Fuller test regression is to

If a series, y, follows a random walk with drift b, what is the optimal one-step ahead forecast of the change in y?

if  a   series, y,   follows   a random   walk   with  drift b,  what's  the   optimal one-step   ahead forecast  of  tjhe change  in   y?

If a series, y, follows a random walk w drift b, wht is da optimal one-step ahead forecast of da change in y?

Which of the following are plausible approaches to dealing with a model that exhibits heteroscedasticity?

ii) Use suitably modified standard errors

iii) Use a generalised least squares procedure

iv) Add lagged values of the variables to the regression equation.

which of  tje  following  are  plausible   approaches   to   dealing   with a model  that   exhibits heteroscedasticity?

i) take logarithms of each of   the variables

ii)  use  suitably modified  standard  errors

iii) use a generalised   least   squares procedure

iv)   add   lagged  values   of   the  variables   to  the regression   equation.

Which of da following r plausible approaches to dealin with a model dat exhibits heteroscedasticity?

iv) Add lagged values of da variables to da regression equation.

Which of the following statements are true concerning the standardised residuals (residuals divided by their respective conditional standard deviations) from an estimated GARCH model?

i) They are assumed to be normally distributed

ii) Their squares will be related to their lagged squared values if the GARCH model is

appropriate

iii) In practice, they are likely to have fat tails

iv) If the GARCH model is adequate, the standardised residuals and the raw residuals

will be identical

which of the following statements  are true  concerning  the standardised residuals  (residuals   divided by their   respective   conditional   standard  deviations)   from  an  estimated  garch   model?

i) they're   assumed  to   be  normally  distributed

ii) their  squares   iwll be related  to  their   lagged   squared  values if   the  garch  model   is

iii)   in   practice,  they're   likely   to   have fat   tails

iv)   if the  garch   model   is  adequate,  the   standardised   residuals and   the   raw  residuals

will be  identical

Which of da following statements r true concerning da standardised residuals (residuals divided by their respective conditional standard deviations) from an estimated GARCH model?

i) They r assumed to b normally distributed

ii) Their squares gon be related to their lagged squared values if da GARCH model is

iii) In practice, they r likely to have fat tails

iv) If da GARCH model is adequate, da standardised residuals and da raw residuals

gon be identical

which one   of the  following   statements is true  concerning  vars?

Which of the following statements is INCORRECT concerning the classical hypothesis testing framework?

whcih   of   the   following   statements   is incorrect  concerning   the classical  hypothesis  testing   framework?

Which of da following statements is INCORRECT concerning da classical hypothesis testing framework?

An ARMA(p,q) (p, q are integers bigger than zero) model will have

an   arma(p,q)   (p, q   are   integers bigger   than   zero)   model  will  have

Suppose that the following regression is estimated using 27 quarterly observations:

$y_t = \beta_1 + \beta_2 x_2 + \beta_3 x_{3t} + u_t$

What is the appropriate critical value for a 2-sided 5% size of test of $H_0: \beta_3 = 1$?

suppose that   the following  regression is  estimated using  27  quarterly  observations:

$y_t  =   \beta_1 +   \beta_2 x_2 +  \beta_3  x_{3t} +   u_t$

what's  thge   apropriate critical   value for a 2-sided 5%  size  of  test of   $h_0:   \beta_3  = 1$?

Suppose dat the following regression is estimated usin 27 quarterly observations:

Wht is da appropriate critical value fa a 2-sided 5% size of test of $H_0: \beta_3 = 1$?

Suppose that two researchers, using the same 3 variables and the same 250 observations on each variable, estimate a VAR. One estimates a VAR(6), while the other estimates a VAR(4). The determinants of the variance-covariance matrices of the residuals for each VAR are 0.0036 and 0.0049 respectively. What is the values of the test statistic for performing a test of whether the VAR(6) can be restricted to a VAR(4)?

suppose  that twpo   researchers,   using  the smae 3  variables  and the  same   250   observations  on   each   variable, estimate  a   var.  one  estimates   a  var(6), while  the   other  estimates   a var(4). the   determinants   of the variance-covariance   matrices  of   the   residuals for  each var are 0.0036   andd   0.0049  respectively.   whta's   the values  of  tghe test  statistic  for performing  a   test  of  whether   the  var(6)  can  be  restricted to a var(4)?

Suppose dat two researchers, usin the same 3 variables and da same 250 observations on each variable, estimate a VAR. One estimates a VAR(6), while da other estimates a VAR(4). Da determinants of da variance-covariance matrices of da residuals fir each VAR r 0.0036 and 0.0049 respectively. Wat is da values of da test statistic fa performing a test of whether da VAR(6) can b restricted to a VAR(4)?

Which of the following is a DISADVANTAGE of using pure time-series models (relative to structural models)?

which   of the   following  is   a   disadvantage  of using pure   time-series models   (relative   to structural models)?

Which of da following is a DISADVANTAGE of usin pure time-series models (relative to structural models)?

Which of the following are alternative names for the dependent variable (usually denoted by y) in linear regression analysis?

(i) The regressand

(ii) The regressor

(iii) The explained variable

(iv) The explanatory variable

which   of the following are  alternative  names  for  the dependent   variable (usually  denoted by  y) in  linear regression analysis?

(i)   the  regressand

(ii)  the   regressor

(iii) the   explained   variable

(iv)   the explanatory variable

Which of da following r alternative names fir the dependent variable (usually denoted by y) in linear regression analysis?

(i) Da regressand

(ii) Da regressor

(iii) Da explained variable

(iv) Da explanatory variable

Which of the following are advantages of the VAR approach to modelling the relationship between variables relative to the estimation of full structural models?

i) VARs receive strong motivation from financial and economic theory

ii) VARs in their reduced forms can be used easily to produce time-series forecasts

iii) VAR models are typically highly parsimonious

iv) OLS can be applied separately to each equation in a reduced form VAR

which  of the   following   are advantages  of   the var  approach   to modelling tje  relationship   between  variables   relative  to  the   estimation  of  full   structural  models?

i)   vars receive   stong motivation  from   financial and  econoimic  theory

ii) vars   in thier   reduced   forms  can be  used easily  to   produce   time-series  forecasts

iii)   var  models are  typically highly parsimonious

iv) ols   can  be   applied   separately   to  eahc   equation   in a reduced   form var

Which of da following r advantages of da VAR approach to modelling da relationship between variables relative to da estimation of full structural models?

ii) VARs in their reduced forms can b used easily to produce time-series forecasts

iii) VAR models r typically highly parsimonious

iv) OLS can b applied separately to each equation in a reduced form VAR

Which of the following statements is TRUE concerning the standard regression model?

which  of the   following statements  is  true  concerning  the   standard regression   modle?

Which of da following statements is TRU concerning da standard regression model?

Consider the following model for $y_t$:

$y_t = \mu + \lambda t + u_t$

Which one of the following most accurately describes the process for $y_t$?

consider  teh   following  model  for  $y_t$:

$y_t  =   \mu +   \lambda t +  u_t$

which   one   of the  following  most accurately describes the process   for  $y_t$?

Consider da following model fir $y_t$:

Which one of da following most accurately describes da process fir $y_t$?

Which of the following is correct concerning logit and probit models?

which  of   the   folowing is correct  concerning  logit and probit  models?

Which of da following is correct concerning logit and probit models?

What is the most important disadvantage of the diagonal VECH approach to building multivariate GARCH models that is overcome by the BEKK formulation?

what's the most   important disadvantage of the  diagonal  vech approach  to building multivariate   garch  models  that's   overcome by the  bekk formulation?

What is da most important disadvantage of da diagonal VECH approach to building multivariate GARCH models dat is overcome by da BEKK formulation?

If a relevant variable is omitted from a regression equation, the consequences would be that:

i) The standard errors would be biased

ii) If the excluded variable is uncorrelated with all of the included variables, all of

the slope coefficients will be inconsistent.

iii) If the excluded variable is uncorrelated with all of the included variables, the

intercept coefficient will be inconsistent.

iv) If the excluded variable is uncorrelated with all of the included variables, all of

the slope and intercept coefficients will be consistent and unbiased but inefficient.

if   a  relevant   variable is   omitted   from a  regression equation,  the  consequeseces  would  be   that:

i)  the  standard errors  would be  biased

ii)  if the   excluded variable   is   uncorrelated   with all   of   teh  included  variables,  all of

the   slope coefficients will be inconsistent.

iii) if   the  excluded   variable is   uncorrelated   with all  of  the  included variables,   the

intercept   coefficient will  be  inconsistent.

iv)   if  the  excluded   variable   is   uncorrelated with   all   of   the included  variables,  all   of

the  slope and  intercept   coefficients   wille   be   consistant and   unbiased  but inefficient.

If a relevant variable is omitted from a regression equation, da consequences would b that:

i) Da standard errors would b biased

ii) If da excluded variable is uncorrelated w/ all of da included variables, alll of

da slope coefficients gon be inconsistent.

iii) If da excluded variable is uncorrelated w all of da included variables, da

intercept coefficient gon be inconsistent.

iv) If da excluded variable is uncorrelated wid all of da included variables, alll of

da slope and intercept coefficients gon be consistent and unbiased but inefficient.

Which of the following are alternative names for the independent variable (usually denoted by x) in linear regression analysis?

(i) The regressor

(ii) The regressand

(iii) The causal variable

(iv) The effect variable

which  of  the  following  are alternative   names  for the independendet variable  (usually   denoted  by x)  in   linear  regression  analysis?

(i) the  regressor

(ii)   teh regressand

(iii)   the   causal  variable

(iv)   the   effect   variable

Which of da following r alternative names fa the independent variable (usually denoted by x) in linear regression analysis?

(i) Da regressor

(ii) Da regressand

(iii) Da causal variable

(iv) Da effect variable

Consider the OLS estimator for the standard error of the slope coefficient. Which of the following statement(s) is (are) true?

(i) The standard error will be positively related to the residual variance

(ii) The standard error will be negatively related to the dispersion of the observations on the explanatory variable about their mean value

(iii) The standard error will be negatively related to the sample size

(iv) The standard error gives a measure of the precision of the coefficient estimate.

consider the   ols  estimator for   the  standard   error of   the  slope coefficient. which of the   following   statement(s) is   (are)  true?

(i)  the  standard  error  will  be  positively   related   to  the   residual variance

(ii) the standard   error will be   negatively related   to  the dispersion  of  the   observations   on  tjhe   explanatory   variable   about   their  mean   value

(iii)   the  standard  error  will   be negatively  related  to   the  sample size

(iv)  the standard   error gives   a  measure  of  thge  precision   of   the coefficient   estimate.

Consider da OLS estimator fir the standard error of da slope coefficient. Which of da following statement(s) is (r) tru?

(i) Da standard error gon be positively related to da residual variance

(ii) Da standard error gon be negatively related to da dispersion of da observations on da explanatory variable bout their mean value

(iii) Da standard error gon be negatively related to da sample size

(iv) Da standard error gives a measure of da precision of da coefficient estimate.

If a Durbin Watson statistic takes a value close to zero, what will be the value of the first order autocorrelation coefficient?

if   a durbin  watson  statistic   takes  a   value  close   to  zero, what  will be   the   value of  the  first   order   autocorrelation coefficient?

If a Durbin Watson statistic takes a value close to zero, wht will b the value of da first order autocorrelation coefficient?

Under the null hypothesis of a Bera-Jarque test, the distribution has

under   the  null  hypothesis of   a bera-jarque  test,   the   distrubution has

Under da null hypothesis of a Bera-Jarque test, da distribution has

if an   estimator   is   said  to   be  consistant,   it's implied   that

Which of the following is a typical characteristic of financial asset return time-series?

which of the  following is   a   typical   charactersistic of   finacial  asset return time-series?

Which of da following is a typical characteristic of financial asset return time-series?

Which of the following assumptions are required to show the consistency, unbiasedness and efficiency of the OLS estimator?

i) $E(u_t) = 0$

ii) $\text{Var}(u_t) = \sigma^2$

iii) $\text{Cov}(u_t, u_{t-j}) = 0 \forall j$

iv) $u_t \sim N(0, \sigma^2)$

which of the   following assumptions   are   required   to  show  the consistency, unbiasedness  adn  efficiency  of the   ols   estimator?

i)  $e(u_t)   =  0$

ii) $\text{var}(u_t)   =  \sigma^2$

iii)   $\text{cov}(u_t,  u_{t-j}) =  0 \forall   j$

iv)   $u_t  \sim   n(0, \sigma^2)$

Which of da following assumptions r required to show da consistency, unbiasedness and efficiency of da OLS estimator?

ii) $\txt{Var}(u_t) = \sigma^2$

iii) $\txt{Cov}(u_t, u_{t-j}) = 0 \forall j$

Which of the following is a disadvantage of the fixed effects approach to estimating a panel model?

which of  the following is  a  disadvantage of   the  fixed   effects approach to  estimating   a panel  model?

Which of da following is a disadvantage of da fixed effects approach to estimating a panel model?

Consider an identical situation to that of question 21, except that now a 2-sided alternative is used. What would now be the appropriate conclusion?

consdider  an   identical   situation   to   that   of question  21, except that nowe   a 2-sided  alternative   is  used. what  would  now  be  ther  appropriate  conclusion?

Consider an identical shyt to dat of question 21, except dat now a 2-sided alternative is used. Wht would now b the appropriate conclusion?

the  price of  a house   is  best described   as   waht  type  of   number?

If a Johansen "trace" test for a null hypothesis of 2 cointegrating vectors is applied to a system containing 4 variables is conducted, which eigenvalues would be used in the test?

if   a johansen "trace" test for   a   null  hypothesis   of   2  cointegrating   vectors  is  applied to  a sytem containing 4 variables   is  conducted,   which   eigenvalues  would  be   unsed in  the test?

If a Johansen "trace" test fa a null hypothesis of 2 cointegrating vectors is applied to a system containing 4 variables is conducted, which eigenvalues would b used in da test?

Which of the following statements is true concerning variance decomposition analysis of VARs?

i) Variance decompositions measure the impact of a unit shock to each of the variables on the VAR

ii) Variance decompositions can be thought of as measuring the proportion of the forecast error variance that is attributable to each variable

iii) The ordering of the variables is important for calculating impulse responses but not variance decompositions

iv) It is usual that most of the forecast error variance for a given variable is attributable to shocks to that variable

which  of the following   statements is true  concerning variance   decomposition   analysis   of vars?

i)   variance decompositions  measure   the impact   of  a unit   shock to   each   of tghe  variables on   the var

ii)   variance   decompositions   can   be  thought   of  as measuring   the  proportion of tghe forecast error   variance  that's  attributable to  each  variable

iii)   the   ordering of  the variables  is   important   for   calculating  impulse   responses   but not   variance  decompositions

iv) it's  usual   tath   most   of   the   forecast   error  variance for a given  variable   is  attributable   to   shocks  to  that variable

Which of da following statements is tru concerning variance decomposition analysis of VARs?

i) Variance decompositions measure da impact of a unit shock to each of da variables on da VAR

ii) Variance decompositions can b thought of as measuring da proportion of da forecast error variance dat is attributable to each variable

iii) Da ordering of da variables is important fir calculating impulse responses but not variance decompositions

iv) It is usual dat most of da forecast error variance fa a given variable is attributable to shocks to dat variable

Suppose that we have estimated a GARCH model for daily equity returns, and we are interested in producing a 10-day forecast of the volatility (measured by the standard deviation of returns) for use in a value at risk model. How could such a forecast most validly be calculated?

suppose  that   we've   esitmated   a  garch   model for  daily   equity returns,   and we're interested in   producing   a   10-day   forecast  of the volatility   (measured by   the standard   deviation   of  returns)   for   use in   a   value  at   risk   model.   how   could   such a   forecast   most validly be   calculated?

Suppose dat we have estimated a GARCH model fir daily equity returns, and we r interested in producing a 10-day forecast of da volatility (measured by da standard deviation of returns) fir use in a value at risk model. How could such a forecast most validly b calculated?

Suppose that the value of $R^2$ for an estimated regression model is exactly zero. Which of the following are true?

i) All coefficient estimates on the slopes will be zero

ii) The fitted line will be horizontal with respect to all of the explanatory variables

iii) The regression line has not explained any of the variability of y about its mean value

iv) The intercept coefficient estimate must be zero.

suppose   that  the  value   of   $r^2$ for  an estimated   regression   model   is exactly zero. which  of  the  following  are   true?

i)   all   coefficient   estimates   on   the  slopes  will  be zero

ii)   the  fitted   line iwll   be   horizontal   wih   respect  to   all   of   teh  explanatory   variables

iii)   the  regression  line hasnt  explained any  of  the variability   of   y about its  mean   value

iv)   the intercept   coefficient estimate must   be zero.

Suppose dat the value of $R^2$ fa an estimated regression model is exactly zero. Which of da following r true?

i) Alll coefficient estimates on da slopes gon be zero

ii) Da fitted line gon be horizontal w respect to alll of da explanatory variables

iii) Da regression line has not explained any of da variability of y bou its mean value

iv) Da intercept coefficient estimate must b zero.

A white noise process will have

(i) A zero mean

(ii) A constant variance

(iii) Autocovariances that are constant

(iv) Autocovariances that are zero except at lag zero

a   white   noise process  will have

(i) a zero   mean

(ii) a  constatn variance

(iii)  autocovariances that are  constant

(iv) autocovariances  that   are   zero except  at  lag zero

A white noise process gon have

(iii) Autocovariances dat are constant

(iv) Autocovariances dat are zero except at lag zero

Which of the following statements are true concerning the Box-Jenkins approach to diagnostic testing for ARMA models?

(i) The tests will show whether the identified model is either too large or too small

(ii) The tests involve checking the model residuals for autocorrelation, heteroscedasticity, and non-normality

(iii) If the model suggested at the identification stage is appropriate, the acf and pacf for the residuals should show no additional structure

(iv) If the model suggested at the identification stage is appropriate, the coefficients on the additional variables under the overfitting approach will be statistically insignificant

which  of   the   following   statements  are true  concerning the  box-jenkins approach   to diagnostic testing for arma   models?

(i)  the tests  will   sohw whether   the identified model  is   either too large  or  too  small

(ii)   the tests involve  checking the   modle  residuals for  autocorrelation, heteroscedasticity,   and  non-normality

(iii) if the modle   suggested  at  the identification stage  is appropriate,   the   acf and pacf for   the residuals should show  no additional  structure

(iv)   if  the  model suggested at the identification   stage is  appropriate,   the   coefficients   on   the additional   variables under the   overfitting approach will   be  statistically   insignificant

Which of da following statements r true concerning da Box-Jenkins approach to diagnostic testing fa ARMA models?

(i) Da tests gon show whether da identified model is either too bigass or too small

(ii) Da tests involve checkin the model residuals fir autocorrelation, heteroscedasticity, and non-normality

(iii) If da model suggested at da identification stage is appropriate, da acf and pacf fa the residuals should show no additional structure

(iv) If da model suggested at da identification stage is appropriate, da coefficients on da additional variables under da overfitting approach gon be statistically insignificant

Which one of the following would be a plausible response to a finding of residual non-normality?

which  one of  the   following would be   a   plausible  response  to   a  finding   of   residual   non-normality?

Which one of da following would b a plausible response to a finding of residual non-normality?

the fixed   effects  panel  model   is also  sometimes   known   as

Which of the following statements is TRUE concerning OLS estimation?

which  of the   following  statements is  true  concerning   ols estimation?

Which of da following statements is TRU concerning OLS estimation?

If the standard tools for time-series analysis, such as estimation of the acf, pacf and spectral analysis, find no evidence of structure in the data, this implies that the data are which of the following?

if   the  standard   tools  for   time-series analysis,  such  as estimation   of the acf,  pacf and spectral  analysis, find   no  evidence  of  structure  in  the   data,   this  implies   that  the data  are which   of the  following?

If da standard tools fa time-series analysis, such as estimation of da acf, pacf and spectral analysis, find no evidence of structure in da data, dis implies dat the data r which of da following?

If two variables, $x_t$ and $y_t$ are said to be cointegrated, which of the following statements are true?

i) $x_t$ and $y_t$ must both be stationary

ii) Only one linear combination of $x_t$ and $y_t$ will be stationary

iii) The cointegrating equation for $x_t$ and $y_t$ describes the short-run relationship

between the two series

iv) The residuals of a regression of $y_t$ on $x_t$ must be stationary

if two   variables, $x_t$  and   $y_t$  are said  to  be  cointegrated,   which   of  the following   statements   are   true?

i)   $x_t$ and   $y_t$ must   both  be  stationary

ii)  only  one linear  combination   of $x_t$   and $y_t$   will be   stationary

iii)  the  cointegrating  equation  for   $x_t$  and  $y_t$   describes the  short-run  relationship

between the two  series

iv)  thge  residuals of  a regression of   $y_t$ on   $x_t$   must   be  stationary

If 2 variables, $x_t$ and $y_t$ r said to b cointegrated, which of da following statements r true?

i) $x_t$ and $y_t$ must both b stationary

ii) Only one linear combination of $x_t$ and $y_t$ gon be stationary

iii) Da cointegrating equation fa $x_t$ and $y_t$ describes da short-run relationship

between da two series

iv) Da residuals of a regression of $y_t$ on $x_t$ must b stationary

A dependent variable whose values are not observable outside a certain range but where the corresponding values of the independent variables are still available would be most accurately described as what kind of variable?

a dependent   variable  whose   values  aren't  observable   outside a  certain   range but where  the  corresponding values of  tje  independent variables   are  still available would be most   accurately  described  as  what  kind   of variable?

A dependent variable whose values r not observable outside a certain range but where da corresponding values of da independent variables r still available would b most accurately described as wht kind of variable?

Under the matrix notation for the classical linear regression model, $y = X \beta + u$, what are the dimensions of $u$?

under   the  matrix  notation for   the   classical linear   regression   model,  $y = x  \beta  +  u$,   what're  the  dimensions  of  $u$?

Under da matrix notation fir the classical linear regression model, $y = X \beta + u$, wat are da dimensions of $u$?

How many parameters will be required to be estimated in total for all equations of a standard form, unrestricted, tri-variate VAR(4), ignoring the intercepts?

how   many  parameters   wiull be  required to   be   estimated  in   total  for all  equations   of a   standard   fomr,  unrestricted, tri-variate  var(4),  ignoring  the  intercepts?

How many parameters gon be required to b estimated in total fir all equations of a standard form, unrestricted, tri-variate VAR(4), ignorin the intercepts?

A researcher tests for structural stability in the following regression model:

$y_t = \beta_1 + \beta_2 x_{2t} + \beta_3 x_{3t} + u_t$

The total sample of 200 observations is split exactly in half for the sub-sample regressions. Which would be the unrestricted residual sum of squares?

a  researcher   tests  for structural   stability  in   the  following   regression model:

$y_t =  \beta_1   +   \beta_2  x_{2t}  +  \beta_3   x_{3t}  +  u_t$

the   total   sample of  200  observations   is  split exactly   in half  for the   sub-sample   regressions. which woudl   be  the unrestricted residual sum   of  squares?

A researcher tests fir structural stability in da following regression model:

Da total sample of 200 observations is split exactly in half fa the sub-sample regressions. Which would b the unrestricted residual sum of squares?

Suppose that we are interested in testing the null hypothesis that a GARCH(2,2) model can be restricted to a process with a constant conditional variance using the likelihood ratio test approach. Which of the following statements are true?

suppose that  we're interested   in testing  the  null hypothesis that   a   garch(2,2)   model  can  be  resticted to  a  process with   a  constant   conditional  variance   using the  likelihood  ratio test   approach. which of  the following  statements  are  true?

Suppose dat we r interested in testing da null hypothesis dat a GARCH(2,2) model can b restricted to a process wid a constant conditional variance usin the likelihood ratio test approach. Which of da following statements r true?

Which one of the following is NOT a plausible remedy for near multicollinearity?

whic  one  of  tjhe following  isn't a  plausible remedy  for   near  multicollinearity?

Which one of da following is NOT a plausible remedy fir near multicollinearity?

Consider the following AR(2) process:

yt = 1.5 yt-1 - 0.5 yt-2 + ut

This is a

consider   the following  ar(2) process:

yt  =  1.5   yt-1  -  0.5 yt-2 +  ut

this is   a

Consider da following AR(2) process:

Dis is a

Which of the following could be used as a test for autocorrelation up to third order?

which of  the   following sould be  used   as   a  test  for   autocorrelation   up to  third   order?

Which of da following could b used as a test fa autocorrelation up to third order?

the  residual  from   a   standard   regression model   is  defined as

If OLS is applied separately to each equation that is part of a simultaneous system, the resulting estimates will be

if  ols  is applied separately   to   each equation  that's   part of   a  simultaneous system,  the  resulting   estimates  will  be

If OLS is applied separately to each equation dat is part of a simultaneous system, da resulting estimates gon be

Which one of the following is NOT an example of mis-specification of functional form?

which  one  of   the following   isn't  an  example  of mis-specification of   functional form?

Which one of da following is NOT an example of mis-specification of functional form?

Suppose that we wished to evaluate the factors that affected the probability that an investor would choose an equity fund rather than a bond fund or a cash investment. Which class of model would be most appropriate?

suppose  that we   wished to  evaluate   the factors  that   affected  the probability that an  investor   would  choose  an equity   fund  rather   than   a   bond   fund or   a  cash  investment.   which class  of   model   woudl be most   appropriate?

Suppose dat we wished to evaluate da factors dat affected da probability dat an investor would claim an equity fund rather den a bond fund or a cash investment. Which class of model would b most appropriate?

Which of the following statements will be true if the number of replications used in a Monte Carlo study is small?

i) The statistic of interest may be estimated imprecisely

ii) The results may be affected by unrepresentative combinations of random draws

iii) The standard errors on the estimated quantities may be unacceptably large

iv) Variance reduction techniques can be used to reduce the standard errors

which   of the  following  statements  wille  be   true  if   the   number   of   replications used  in   a   monte  carlo  study   is  small?

i)  the statistic of   interest  may   be estimated   imprecisely

ii)  the  results may  be  affected   by  unrepresentative   combinations  of random  draws

iii) the  standard  errors   on the  estimated  quantities may   be   unacceptably large

iv) variance  reduction techniques can  be   used   to   reduce the   standard errors

Which of da following statements gon be tru if da number of replications used in a Monte Carlo study is small?

i) Da statistic of interest may b estimated imprecisely

ii) Da results may b affected by unrepresentative combinations of random draws

iii) Da standard errors on da estimated quantities may b unacceptably bigass

iv) Variance reduction techniques can b used to reduce da standard errors

Which of the following is a disadvantage of the random effects approach to estimating a panel model?

which   of the following   is a  disadvantage of  the   random effects   approach   to  estimating  a   panel  modle?

Which of da following is a disadvantage of da random effects approach to estimating a panel model?

Which of the following could result in autocorrelated residuals?

i) Slowness of response of the dependent variable to changes in the values of the independent variables

ii) Over-reactions of the dependent variable to changes in the independent variables

iii) Omission of relevant explanatory variables that are autocorrelated

iv) Outliers in the data

which of   the  folowing   could  result   in   autocorrelated residuals?

i)   slowness   of  reponse   of  the  dependent variable  to   changes in the values of   the independent  variables

ii)   over-reactions of   tje dependent   variable  to changes  in  the indipendent   variables

iii)  omission   of relevant   explanatory  variables  that   are autocorrelated

iv)  outliers in  the   data

Which of da following could result in autocorrelated residuals?

i) Slowness of response of da dependent variable to changes in da values of da independent variables

ii) Over-reactions of da dependent variable to changes in da independent variables

iii) Omission of relevant explanatory variables dat are autocorrelated

iv) Outliers in da data

Which of the following statements are true concerning the acf and pacf?

(i) The acf and pacf are often hard to interpret in practice

(ii) The acf and pacf can be difficult to calculate for some data sets

(iii) Information criteria represent an alternative approach to model order determination

(iv) If applied correctly, the acf and pacf will always deliver unique model selections

which   of the following statements   are   true concerning   the acf adn  pacf?

(i) hten acf  and   pacf   are   often  hard   to  interpret  in   practice

(ii)   the  acf and   pacf  can  be  difficult  to calculate   for some  data sets

(iii)   information  criteria   represent  an alternative   approach   to  modle  order  determination

(iv)   if aplied   correctly,   the  acf   and   pacf wille   always deliver   unique  model   selections

Which of da following statements r true concerning da acf and pacf?

(i) Da acf and pacf r often hard to interpret in practice

(ii) Da acf and pacf can b difficult to calculate fir some data sets

(iv) If applied correctly, da acf and pacf gon always deliver unique model selections

Which of the following conditions are necessary for a series to be classifiable as a weakly stationary process?

(i) It must have a constant mean

(ii) It must have a constant variance

(iii) It must have constant autocovariances for given lags

(iv) It must have a constant probability distribution

which  of the following conditions   are  necessary for  a  series to be  classifiable as   a   weakly stationary process?

(i) it   must've  a   constant mean

(ii)  it must've   a constant variance

(iii) it  must've   constatn   autocovariances   for  given  lags

(iv)   it   must've a constant  probability  distribution

Which of da following conditions r necessary fir a series to b classifiable as a weakly stationary process?

(iii) It must have constant autocovariances fa given lags

Consider the following equation and determine the class of model that it best represents

$y_{it} = \alpha + \beta_{it} + \mu_i + \nu_{it}$

consider  the following  equation anbd  determine  the  class of   model  that it  best  represents

$y_{it}   =  \alpha + \beta_{it} + \mu_i + \nu_{it}$

Consider da following equation and determine da class of model dat it best represents

Note that statistical tables are not necessary to answer this question. For a sample of 1000 observations, the Dickey-Fuller test statistic values are

onot that   statistical   tables  aren't  necessary to   answer  this question. for  a  sample   of   1000 observations,   the dickey-fuller test statistic  values   are

Note dat statistical tables r not necessary to answer dis question. Fir a sample of 1000 observations, da Dickey-Fuller test statistic values are

Suppose that a hypothesis test is conducted using a 5% significance level. Which of the following statements are correct?

(i) The significance level is equal to the size of the test

(ii) The significance level is equal to the power of the test

(iii) 2.5% of the total distribution will be in each tail rejection region for a 2-sided test

(iv) 5% of the total distribution will be in each tail rejection region for a 2-sided test.

suppose that  a   hypothesis  test  is  conducted  useing  a   5% significance level.  which of  the folowing  statements   are correct?

(i)   the  significance  level is   equal   to   the   sieze  of   the   test

(ii)  the  significance  level  is equal   to  the  power of the   test

(iii) 2.5% of the  total distribution  will   be in each   tail rejection   region   for   a  2-sided  test

(iv) 5%   of   the  total   distribution wiull  be  in each  tail  rejection region  for  a   2-sided   test.

Suppose dat a hypothesis test is conducted usin a 5% significance level. Which of da following statements r correct?

(i) Da significance level is equal to da size of da test

(ii) Da significance level is equal to da power of da test

(iii) 2.5% of da total distribution gon be in each tail rejection region fa a 2-sided test

(iv) 5% of da total distribution gon be in each tail rejection region fir a 2-sided test.

Which one of the following criticisms of the Dickey-Fuller/Engle-Granger approach to dealing with cointegrated variables is overcome by the Engle-Yoo (EY) procedure?

which   one   of   the  following criticisms   of   the dickey-fuller/engle-granger  approach  to  dealing with cointegrated variables is  overcome by the  engle-yoo (ey) proceedure?

Which one of da following criticisms of da Dickey-Fuller/Engle-Granger approach to dealin with cointegrated variables is overcome by da Engle-Yoo (EY) procedure?

Consider a series that follows an MA(1) with zero mean and a moving average coefficient of 0.4. What is the value of the autocovariance at lag 1?

consider a  series  that   follows   an   ma(1)   with zero mean  and a  moving average   coefficient   of  0.4.  what's the   value of the autocovariance  at lag  1?

Consider a series dat follows an MA(1) wid zero mean and a movin average coefficient of 0.4. Wht is da value of da autocovariance at lag 1?

Which of the following estimation techniques are available for the estimation of over-identified systems of simultaneous equations?

i) OLS

ii) ILS

iii) 2SLS

iv) IV

whcih  of the  following estimation techniques are   available for the  estimation of  over-identified   systems of  simultaneous   equations?

i)   ols

ii)   ils

iii) 2sls

iv)  iv

Which of da following estimation techniques r available fir the estimation of over-identified systems of simultaneous equations?

i) OLS

ii) ILS

iii) 2SLS

iv) IV

Which one of the following statements best describes a Type II error?

which  one  of   the following statements   best  describes   a  type ii  error?

Which one of da following statements best describes a Type II error?

Which one of the following would be the most appropriate as a 95% (two-sided) confidence interval for the intercept term of the model given in question 21?

which   one  of the  follwoing would  be   the  most  appropriate as   a  95% (two-sided)  confidence   interval  for   thge intercept  term of the   model   given   in  question 21?

Which one of da following would b the most appropriate as a 95% (two-sided) confidence interval fir the intercept term of da model given in question 21?

Which of the following are characteristics of a stationary process?

i) It crosses its mean value frequently

ii) It has constant mean and variance

iii) It contains no trend component

iv) It will be stationary in first difference form

which  of tje following  are  characteristics of  a   stationary  process?

i)   it crosses its   mean value  frequently

ii) it  has  constant mean  and variance

iii)  it   contains  no   trend  component

iv) it'll be   stationary  in first   difference  form

Which of da following r characteristics of a stationary process?

iv) It gon be stationary in first difference form

Consider again the VAR model of equation 16. Which of the following conditions must hold for it to be said that there is bi-directional feedback?

consider again the   var   model   of  equation  16. which of   teh  following  conditions  must  hold  for  it  to   be   said that there's   bi-directional   feedback?

Consider again da VAR model of equation 16. Which of da following conditions must hold fa it to b said dat there is bi-directional feedback?

Consider the following sample autocorrelation estimates obtained using 250 data points:

1) Lag 1 2 3

2) Coefficient 0.2 -0.15 -0.1

3) Assuming that the coefficients are approximately normally distributed, which of the coefficients are statistically significant at the 5% level?

consider  the  following   sample  autocorrelation  estimates   obtained  using 250 data  points:

1)  lag  1 2   3

2)   coefficient   0.2 -0.15  -0.1

3)   assuming   that  the   coefficients   are  approximately  normally  distributed, which  of   the coefficients  are  statistically   significant at   the 5%  level?

Consider da following sample autocorrelation estimates obtained usin 250 data points:

1) Lag 1 2 3

3) Assuming dat the coefficients r approximately normally distributed, which of da coefficients r statistically significant at da 5% level?

Which one of the following is examined by looking at a goodness of fit statistic?

which one   of the  follwoing   is examined  by   looking   at a  goodness  of   fit   statistic?

Which one of da following is examined by lookin at a gawd of fit statistic?

Which of the following statements are correct concerning the use of antithetic variates as part of a Monte Carlo experiment?

i) Antithetic variates work by reducing the number of replications required to cover the whole probability space

ii) Antithetic variates involve employing a similar variable to that used in the simulation, but whose properties are known analytically

iii) Antithetic variates involve using the negative of each of the random draws and repeating the experiment using those values as the draws

iv) Antithetic variates involve taking one over each of the random draws and repeating the experiment using those values as the draws

which   of the folowing  statements  are  correct  concerning the   use   of   antithetic variates as   part of  a  monte   carlo experiment?

i) antithetic   variates   work by   reducing the  number  of   replications required  to   cover the  whole probability   space

ii)  antithetic variates  involve  employing a   similar   variable   to   that   used  in  tjhe  simulation,   but whose  properties  are known analytically

iii) antithetic  variates  involve   useing the   negative of   each  of  the random  draws   adn   repeating   the   experiment  using those   values  as   the   draws

iv)   antithetic   variates involve  taking  one over  each of the  random  draws and repeating  the  experiment  using   those  values as  tghe   draws

Which of da following statements r correct concerning da use of antithetic variates as part of a Monte Carlo experiment?

i) Antithetic variates wrk by reducing da number of replications required to cover da whole probability space

ii) Antithetic variates involve employing a similar variable to dat used in da simulation, but whose properties r known analytically

iii) Antithetic variates involve usin the negative of each of da random draws and repeating da experiment usin those values as da draws

iv) Antithetic variates involve takin one ova each of da random draws and repeating da experiment usin those values as da draws

Which one of the following statements is true concerning alternative forecast accuracy measures?

whic  one  of teh  following   statements is true  conserning   alternative  forecast   accuracy measures?

Which one of da following statements is tru concerning alternative forecast accuracy measures?

Which of the following criticisms of standard ("plain vanilla") GARCH models can be overcome by EGARCH models?

i) Estimated coefficient values from GARCH models may be negative

ii) GARCH models cannot account for leverage effects

iii) The responsiveness of future volatility to positive and negative shocks is symmetric under a GARCH formulation

iv) GARCH models cannot allow for a feedback from the volatility to the returns

wich   of  the  following criticisms of   standard   ("plain  vanilla")   garch   models can be  overcome  by egarch models?

i) estimated   coefficient values from   garch  models  may be   negative

ii) garch models can't   account   for leverage   effects

iii)  the responsiveness  of   future  volatility   to   positive  and negative   shocks  is symmetric under a  garch formulation

iv) garch   models   can't allow   for   a feedback   from the   volatility to   the   returns

Which of da following criticisms of standard ("plain vanilla") GARCH models can b overcome by EGARCH models?

i) Estimated coefficient values from GARCH models may b negative

ii) GARCH models cannot account fir leverage effects

iii) Da responsiveness of future volatility to positive and negative shocks is symmetric under a GARCH formulation

iv) GARCH models cannot allow fa a feedback from da volatility to da returns

Suppose that 100 separate firms were tested to determine how many of them "beat the market" using a Jensen-type regression, and it is found that 3 fund managers significantly do so. Does this suggest prima facie evidence for stock market inefficiency?

suppose   that  100  seperate   firms   were tested   to determine  how many of  them "beat   the market" using  a   jensen-type regression,   and   it's   found   that  3 fund managers significantly   do so.   doens this   suggest   prima   facie  evidence  for stock market   inefficiency?

Suppose dat 100 separate firms were tested to determine how many of em "beat da market" usin a Jensen-type regression, and it is found dat 3 fund managers significantly do soo. Does dis suggest prima facie evidence fa stock market inefficiency?

Which of the following are advantages of the use of panel data over pure cross-sectional or pure time-series modelling?

(i) The use of panel data can increase the number of degrees of freedom and therefore the power of tests

(ii) The use of panel data allows the average value of the dependent variable to vary either cross-sectionally or over time or both

(iii) The use of panel data enables the researcher allows the estimated relationship between the independent and dependent variables to vary either cross-sectionally or over time or both

which  of the   following  are   advantages of the use of panel data over  pure cross-sectional  or  pure  time-series   modelling?

(i)   tje  use  of panel   data can  increase   thge number   of degrees of freedom   and therefore  the power   of tests

(ii)  the use  of   panel data   allows   the average   value of   the   dependent variable   to  vary  eiter  cross-sectionally   or over  time  or both

(iii)   the use  of panel   data   enables the researcher  allows  hten   estimated  relationship between the   independent   and  dependent   variables  to vary   either cross-sectionally   or over   time or   both

Which of da following r advantages of da use of panel data ova pure cross-sectional or pure time-series modelling?

(i) Da use of panel data can increase da number of degrees of freedom and therefore da power of tests

(ii) Da use of panel data allows da average value of da dependent variable to vary either cross-sectionally or ova time or both

(iii) Da use of panel data enables da researcher allows da estimated relationship between da independent and dependent variables to vary either cross-sectionally or ova time or both

If the Engle-Granger test is applied to the residuals of a potentially cointegrating regression, what would be the interpretation of the null hypothesis?

if the  engle-granger test is  aplied  to  the residuals of a   potentially   cointegrating regression,   what would be  the   interpretation  of the null   hypothesis?

If da Engle-Granger test is applied to da residuals of a potentially cointegrating regression, wht would b the interpretation of da null hypothesis?

Which of the following statements are true concerning the autocorrelation function (acf) and partial autocorrelation function (pacf)?

i) The acf and pacf will always be identical at lag one whatever the model

ii) The pacf for an MA(q) model will in general be non-zero beyond lag q

iii) The pacf for an AR(p) model will be zero beyond lag p

iv) The acf and pacf will be the same at lag two for an MA(1) model

which of the   following statements  are true   concerning  the   autocorrelation function  (acf) anbd  partial  autocorrelation  function (pacf)?

i)  tjhe acf  and  pacf iwll   always  be   identical at lag one  whatever  the  model

ii) the  pacf  for  an  ma(q) model will in general   be   non-zero beyond  lag  q

iii)  the pacf for   an   ar(p)   model  will   be  zero   beyond  lag p

iv)   the  acf   and pacf will  be   the  same at   lag two  for an ma(1) model

Which of da following statements r true concerning da autocorrelation function (acf) and partial autocorrelation function (pacf)?

i) Da acf and pacf gon always b identical at lag one w.e the model

ii) Da pacf fa an MA(q) model gon in general b non-zero beyond lag q

iii) Da pacf fir an AR(p) model gon be zero beyond lag p

iv) Da acf and pacf gon be da same at lag 2 for an MA(1) model

B

Question: Suppose that a researcher wishes to test the null hypothesis that the true value of the intercept coefficient is zero. What would be the appropriate test statistic?
A. t-statistic
B. F-statistic
C. t-rho statistic
D. F-rho statistic
Answer: A

B

Question: Suppose that a researcher wishes to use information criteria to determine the optimal lag length for a VAR. 500 observations are available for the

B

Question: Which of the following statements is true?

(i) The OLS estimator is unbiased

(ii) The OLS estimator is consistent

(iii) The OLS estimator is efficient

(iv) The OLS estimator is asymptotically normal

(v)

B

Question: Suppose that a researcher wishes to use information criteria to determine the optimal lag length for a VAR. 500 observations are available for the bi-variate VAR, and the values of the determinant of the variance-

C

(i) The OLS estimator is biased if the error terms are heteroscedastic

(

B

Question: The following regression model is estimated using OLS

y = β0 + β1 x1 + β2 x2 + β3 x3 + β4 x4 + β5 x5 + β6 x6

B

Question: Suppose that a researcher wishes to test the null hypothesis that the population mean of a variable is zero. The researcher uses a significance level of 0.05. The researcher obtains a sample mean of 0.5. What

A

A. The sum of the squared residuals is a biased estimator of the variance of the disturbances
B. The sum of the squared residuals is a biased estimator of the variance of the disturbances
C. The sum

D

Question: Suppose that a researcher wishes to use information criteria to determine the optimal lag length for a VAR. 500 observations are available for the bi-variate VAR, and the values of the determinant of the variance-covariance matrix of residuals are 0.0336,

A

B. The sum of the squared residuals is a consistent estimator of the variance of the disturbances
C. The sum

D

Question: Which of the following statements is FALSE?
A. The sum of the squared residuals is a measure of the total sum of squares
B. The sum of the squared residuals is a measure of the explained sum of squares
C. The sum of the squared

D

i) The sum of the squared residuals is a measure of the total sum of squares

ii) The sum of the squared residuals is a measure of the explained sum of squares

iii) The sum of the squ

A

Question: Which of the following is not a characteristic of a stationary process?
A. The mean and variance are constant over time
B. The mean and variance are not constant over time
C. The mean and variance are constant over time
D. The mean and variance are not constant over time

D

A. The ARCH(q) model is more parsimonious than the GARCH(1,1) model
B. The ARCH(q) model is more likely to violate non-negativity constraints than the GARCH(

A

Question: Suppose that the following regression model is estimated by OLS:

y = β0 + β1 x1 + β2 x2 + β3 x3 + ε

where x1, x2, and x3 are the explanatory variables and ε is the error term

C

Question: Which of the following is not a property of the OLS estimator?
A. It is unbiased
B. It is consistent
C. It is efficient
D. It is asymptotically normal
Answer: B

Question: Which of the following is not a property of

B

B

C. The sum

A

A. The sum of the coefficients of the regression of y on x is equal to the sum of the coefficients of the regression of x on y
B. The sum of the coefficients of the regression of y on x

C

C. The sum

A

Question: Which one of da following statements is true concerning the use of VARs?
A. VARs are usually used to forecast da future values of a variable
B. VARs are usually used to forecast da future values of a variable and to explain da past values of a variable

B

Question: Which of the following statements is INCORRECT concerning the classical hypothesis testing framework?
A. If the null hypothesis is rejected, the alternative is accepted
B. The null hypothesis is the statement being tested while the alternative encompasses the remaining outcomes of interest
C. The test of significance and confidence

D

(i) The sum of the squared residuals is a measure of the total variation in the dependent variable

(ii) The sum of the squared residuals is a measure of the total variation in the dependent variable

(

A

A. The sum of the squared residuals is a measure of the total variation in the dependent variable
B. The sum of the squared residuals is a measure of the total variation in the dependent variable
C. The sum of the

D

Question: Which of the following statements are true?

i) The OLS estimator is unbiased

ii) The OLS estimator is consistent

B

Question: Which of the following is not a disadvantage of the random effects approach to estimating a panel model?
A. The model is likely to be technical to estimate
B. The approach may not be valid if the composite error term is correlated with one or more of the explanatory variables
C

B

A. The sum of the squares of the residuals is a measure of the goodness of fit of the regression line
B. The sum of the squares of the residuals is a measure of the goodness of fit of the regression line
C.

A

A

Question: Consider the following AR(2) process:

Dis is a
A. Stationary process
B. Unit root process
C. Explosive process
D. Stationary

D

(i) The sum of the squared residuals is a biased estimator of the variance of the disturbances

(ii) The sum of the squared residuals is a consistent estimator of the variance of the disturbances

D

C. The sum

A

Question: Suppose that a researcher wishes to test the hypothesis that the slope of the regression line is zero. Which of the following statements is true?
A. The researcher should use the t-test for the slope of the regression line
B. The researcher should use the F-test for the

A

A. The sum of the squares of the residuals is a measure of the goodness of fit of the model
B. The sum of the squares of the residuals is a measure of the goodness of fit of the model
C. The sum

A

Question: Which one of the following statements is true?
C. The sum of the squared residual

A

A. The OLS estimator of the slope coefficient is unbiased
B. The OLS estimator of the slope coefficient is consistent
C. The OLS estimator of the slope coefficient is efficient
D. The OLS estimator of

D

C. The sum of the squared residuals

A

A. The sum of the squared residuals is a measure of the goodness of fit of the regression line.
B. The sum of the squared residuals is a measure of the goodness of fit of the regression line.
C

A

Question: Suppose that a researcher wishes to test the hypothesis that the slope of the regression of y on x is zero. Which of the following tests should be used?
A. F-test
B. T-test
C. Z-test
D. F-test
Answer:

D

Question: Consider the following regression model

yt = β0 + β1 x1 + ut

where x1 is a vector of explanatory variables and ut is a vector of disturbances. The disturbances are assumed to be i.i.d. with zero mean and variance σ2

D

D

C. The sum

A

A. The OLS estimator of the slope coefficient in a regression model is unbiased
B. The OLS estimator of the slope coefficient in a regression model is consistent
C. The OLS estimator of the slope coefficient in a regression

A

A. The OLS estimator of the slope coefficient in a regression model is unbiased.
B. The OLS estimator of the slope coefficient in a regression model is consistent.
C. The OLS estimator of the slope coefficient in

D

C

D

(i) The OLS estimator of the slope coefficient is unbiased

(ii) The OLS estimator of the slope coefficient is consistent

(iii) The OLS estimator of the slope coefficient is efficient

(

B

A. The Dickey-

C

A. The sum of the squared residuals is a measure of the total sum of squares of the model.
B. The sum of the squared residuals is a measure of the total sum of squares of the model.
C

D

A. The variance of the error term in a regression model is always smaller than the variance of the disturbance term

B. The variance of the error term in a regression model is always larger than the variance of the disturb

D

B. The variance of the error term in a regression model is always larger than the variance of the disturbance term

A

Question: Suppose that a researcher wishes to test the hypothesis that the slope of the regression of y on x is zero. Which of the following tests would be the most appropriate?
A. The F-test
B. The t-test
C. The chi-squared test
D

D

Question: Which of the following statements are true concerning the autocorrelation function (acf) and partial autocorrelation function (pacf)?

ii) The pacf for an MA

A

Question: Suppose that a test that the true value of the intercept coefficient is zero results in rejection. What would be the appropriate conclusion?
A. Drop the intercept and re-run the regression
B. Retain the intercept
C. Re-compute the test statistic
D. The

B

A. The Dickey-Fuller test is a test for the presence of a unit root in a time series
B. The Dickey-Fuller test is a test for the presence of a unit root in a regression
C

A

Question: Which of the following statements is false?
A. The OLS estimator of the slope coefficient is BLUE
B. The OLS estimator of the slope coefficient is the best linear unbiased estimator
C. The OLS estimator of the slope coefficient is the best linear unbiased

D

i) The OLS estimator of the variance of the disturbances is unbiased

ii) The OLS estimator of the variance of the disturbances is inefficient

iii) The OLS estimator of the variance of the disturb

C

(i) The coefficient of determination is always less than 1

(ii) The coefficient of determination is always greater than 1

(iii) The coefficient of determination is always equal to 1

(iv) The coefficient of determination

D

Question: Which of the following statements are true concerning the Box-Jenkins approach to diagnostic testing for ARMA models?

(ii) The tests involve checking the model residuals for autocor

C

Question: Suppose that the following regression model is estimated by ordinary least squares:

where   is the error term,   is the intercept,   is the slope coefficient, and   is the disturbance term.

If the regression is estimated by ordinary least squares, then

A

Question: Suppose that a researcher wishes to use information criteria to determine the optimal lag length for a

B

A

A. The coefficient of determination is always less than or equal to 1

D

C. The