text:
- Pulmonary Disease Iowa City, IA
Overall Rating
27 Patient Reviews
- Friendly Staff
- Promptness
- Wait Time 20.9 minutes
- Appropriate Followup
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- Bedside Manner
- Fair and Accurate Diagnosis
- Easy Appointments
Showing 1 - 12 reviews
5.0 of 5
September 2nd, 2016
1.0 of 5
January 21st, 2016
Dr. Vacca misdiagnosed me with end stage emphysema and had me so depressed with his rotten attitude and bad diagnosis. I only had a lung infection from carbon monoxide poisoning and needed be on prednisone briefly and oxygen therapy. When a good pulmonary doctor examined me, he took me off all pills and oxygen and I started feeling better. Vacca refused to have my oxygen tanks picked up because his wife worked at the company they come from. What a cheat, liar and total creep. Marinette Hospital was happy to get rid of him! Don't trust anything he says, get a second opinion.
1.0 of 5
July 9th, 2014
Gave me a pill that i KNEW would cause candida-he promised it wouldnt-IT DID!He refused to see me and treat the candida-REAL JERK
1.0 of 5
March 22nd, 2014
I would not recommend this Dr. to anyone.
This Doctor told me I had Pulmonary Fibrosis and had five to ten years to live and when my Rheumatologist tried to get in touch with him and talk with him about me having lung biopsy's done to find out for sure what I had he would not speak with him. I finally decided to find a different Doctor and when they did lung biopsy's I had Sarcoidosis instead of Fibrosis. I also witnessed him being very hateful to his office help and half the people that I know who have gone to this man feel the same way about him. I am very disappointed in Good Sam for putting him over anything at the hospital. He has run off several good Doctor's from there already. There are Doctor's out there who care about their patient more than their ego.
5.0 of 5
October 22nd, 2012
I am 20 and have breathing problems. A lot of times when i go to the DR. i find it very frustrating because think because im so young i dont care about my health, so they talk over my head and when i ask questions they dont answer the question. But this is not the case with Dr.Vacca, he talked on my level exp what is causing the asthma and how in the long run he will help correct it. And he has, i always find it relieving when i leave that office.
5.0 of 5
May 13th, 2012
This doc is topnotchHe is only concerned about his patientsHe answers all questions at my level -
5.0 of 5
March 6th, 2011
Dr. tells it straight and has the best interest of his pt's at heart.
I have breathing problems. Dr. Vacca talks to me directly and clearly. He wants me to be better and knows how to get me there. He motivates me to take care of my health. I have full confindence in him.
5.0 of 5
March 2nd, 2011
He made seem a little rough around the edges put he has a soft side to him people just need to look deep down inside
5.0 of 5
March 2nd, 2011
I would reccomend this doctor to anyone. He is very knowledgeable and very caring to his patients. The wait time in the office is very minimal and he has a caring and polite office staff. I think in any situation I would want Dr. Vacca to take care of me!
5.0 of 5
March 1st, 2011
I am a nurse and have worked with this doctor. He is the only physician I will allow to provide healthcare to myelf or my family. He is one of the very few compasionate physicians in the medical field. Every decision that he makes is based solely on the patient's best interest.
1.0 of 5
October 22nd, 2010
I would not refer ANYONE to this Dr. He does not treat patients or family in a decent manner, He is always short tempered, annoyed if you have questions, degrades other Drs in the same hospital and clearly does not have any morals or ethics. I would rather go to a veteranarian than this man that calls himself a professional!!!
5.0 of 5
July 24th, 2017
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Okra and Stewed Tomatoes
Be the first to review
READY IN: 30mins
Recipe by LeeDelaino
My friends always like this when I make it because the (sometimes) slimy texture of okra is not reallly noticeable when it's stewed. The only real difference in this from what my Granny makes is the addition of red wine vinegar--she would have added white.
See Discussion:
Ingredients Nutrition
- 1 large onion, diced
- 3 tablespoons oil
- 4 cups okra, cleaned and sliced in 1/2 chunks
- 1 (28 ounce) can diced tomatoes
- 2 minced garlic cloves
- 1 teaspoon red wine vinegar
- 1 teaspoon brown sugar
- salt, to taste
- pepper, to taste
- Tabasco sauce, to taste
Directions See How It's Made
- Sauté onion on medium heat until soft and add okra and stir to coat allowing okra to warm through.
- Add tomatoes, garlic, vinegar and sugar. Turn heat to medium high and bring to a boil for 1 minute.
- Turn heat to low and cook until okra is desired texture.
- Add seasonings to taste.


text:
Name: Serena Darino Enrollment number: 064597
Contents .......................................................................................................................................... 2 Introduction .................................................................................................................................... 3 1.
Graphs and summary statistics ................................................................................................. 4
Data analysis ............................................................................................................................ 8 2.1.
Correlogram...................................................................................................................... 8
Testing for unit roots ......................................................................................................... 9
Cointegration .................................................................................................................. 13
Testing for non-linearity.................................................................................................. 14
Model selection............................................................................................................... 15
ARCH-GARCH model .......................................................................................................... 17
Diagnostic checking ............................................................................................................... 21
Representation ................................................................................................................ 21
Residual graphs ............................................................................................................... 22
Correlograms .................................................................................................................. 23
Remaining ARCH effects................................................................................................ 24
Testing for normality ...................................................................................................... 25
Forecasting ............................................................................................................................ 27
Conclusions .................................................................................................................................. 33
Introduction Volatility in exchange rates is, during the last years, at the centre of the theoretical and empirical economic and financial analysis. The first reason is represented by the phenomenon's growing importance, thanks to the increased international financial markets' integration. The second reason is that volatility in exchange rates has a relevant influence on the assets' riskiness. Models based upon the past observed variance are unable to capture the accentuated variability that characterizes these kind of data. Considering also that, in the agents' decisional processes, it is not the unconditional variance that enters, but the conditional variance, which is a measure of the time uncertainty of the evaluated variable. Furthermore, volatility is not constant but used to change over time1. Empirical analysis show that exchange rates series are characterized by heteroskedasticity, nonlinearity
heteroskedastic) family models let us to deal with these data features since it does not assume that the variance is constant over time and which describes how the variance of the errors evolves. In this way it is possible to study volatility in exchange rates. Data used in this report come from the statistical data warehouse of the European Central Bank. They are nominal effective exchange rates (that is a summery measure of the external relative strength of a currency against the Euro), monthly observations from January 1999 to November 2015 (total amount of 203 observations for each series), weighting aggregation method, not seasonally adjusted. The three strongest and more traded currencies are examined here: the US Dollar, the UK Pound Sterling and the Japanese Yen. The following analysis is developed complying with the Chris Brooks' Introductory Econometric for Finance and using the EViews software to compute estimations. You will find, through the test, in italic in brackets, the names of the corresponding outputs in the associated EViews workfile. I first make a graphical and statistical analysis of the data, testing for the main basic assumption; then I proceed to the model estimation. After this, I run some diagnostic tests to check out the model's accuracy and then I perform a brief forecasting exercise.
E. Rossi, 1995.
1. Graphs and summary statistics2 The starting point is represented by the effective exchange rates series of the three strongest currencies against Euro: Japanese Yen, US Dollar and UK Pound Sterling3. Observing the line graph and the summary statistics of each series we can see that the relative strength of UK Pound Sterling (uk_pound_ster_linegraph and uk_pound_ster_stat) rises starting from 2007. It reaches the maximum values during 2009 (the absolute maximum value of 0.91966 on March 2009) and then it decreases slightly till today, when it approximately settles around the mean value. The standard deviation, in the amount of 0.093908, is low enough, so the observations are not characterized by great jumps in average. UK pound sterling .95 .90 .85 .80 .75 .70 .65 .60 .55 2000
Series: UK_POUND_STERLING Sample 1999M01 2015M11 Observations 203
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
0.738084 0.700450 0.919660 0.589330 0.093908 0.297874 1.694123
Jarque-Bera Probability
17.42611 0.000164
0 0.60
2 3
Page "data_analysis� in the EViews workfile. From forexitalia24.com
Regarding US Dollar (us_dollar_linegraph and us_dollar_stat), from the absolute minimum of 0.8532 on June 2001, it grows up to 1.577 on July 2008. After this, the series shows several drifts to end with a considerable decrease during 2014 and the last months of 2015. The standard deviation is not so high, but a little bigger than the UK Pound Sterling's one. US dollar 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9 0.8 2000
Series: US_DOLLAR Sample 1999M01 2015M11 Observations 203
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
1.218957 1.267300 1.577000 0.853200 0.179045 -0.448346 2.341505
Jarque-Bera Probability
10.46864 0.005330
0 0.9
Finally, about Japanese Yen (jap_yen_linegraph and jap_yen_stat) we can see that the distribution is characterized by a significant higher standard deviation (in the amount of 17.795); the maximum relative strength of the currency is reached on July 2008 with a value of 168.45 while the minimum is touched on October 2000 with a value of 92.74, but we must consider that a lot of local minimum and maximum points are present throughout all the distribution.
Japanese yen 170 160 150 140 130 120 110 100 90 2000
Series: JAPANESE_YEN Sample 1999M01 2015M11 Observations 203
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
127.8773 130.3400 168.4500 92.74000 17.79511 0.176312 2.460292
Jarque-Bera Probability
3.515527 0.172430
0 100
Examining the skewness and kurtosis values we can see that UK Pound Sterling and Japanese Yen's distributions have a positive skewness, so they have the tail on the right side longer than the left one, even if they are not so greater than zero so they are not so different from the normality; US Dollar instead has a negative skewness value, hence its left side tail is longer than the right side. All the kurtosis values are less than 3 (the normal distribution's kurtosis): the distributions are platykurtic, with a lower, wider peak around the mean and thinner tails. However, the Japanese Yen has the smallest differences from the normal distribution. In fact, the associated probability of the Jarque-Bera test for Japanese Yen leads not to reject the null hypothesis of a normal distribution; on the other hand, for both US Dollar and UK Pound Sterling we reject the null even at 1% level.
Now, we can observe the line graph with the normalized values of the effective exchange rates of the three series (yen_dollar_ster_normgr) to check out any similar path. 2
-2 2000
Japanese yen US dollar UK pound sterling
It shows that, for almost the first part of the sample, US Dollar and Japanese Yen follow a similar trend. During 2008 and 2009 they start to differ from one another and in the second part of the sample we observe that US Dollar and UK Pound Sterling's drifts are more similar, while Japanese Yen gradually detaches. All the three series are characterized by evident volatility, more highlighted during 2007-2008 (the years of the financial crisis) and during 2012-2013 for Japanese Yen only.
2. Data analysis4 I can now proceed to analyze some specific characteristics of the series in order to come to the most appropriated model to describe the data. 2.1.
The observation of the series' correlogram let us to consider the autocorrelation function (ACF) and the partial autocorrelation function (PACF). I choose to plot the correlogram of the raw series (level) and I specify the highest order of lag in the amount of 12 (since I have monthly observations). All the three correlograms (jap_yen_correlogram, uk_pound_ster_correlogram and us_dollar_correlogram) show a geometrically decaying acf and a pacf that cuts off close to zero at lag 3. In fact, in all the three cases, only the first two pacf coefficients are significant. The QStatistics and their p-values lead to the rejection of the null hypothesis that there is no autocorrelation up to order 12.
Page "data_analysis� in the EViews workfile.
This should suggests to estimate an AR(2) seeing as we have a decaying acf and two non zero pacf coefficients. 2.2.
Testing for unit roots
But the autocorrelation functions suggest also the presence of non-stationary and this is confirmed by the evidence that all the acf coefficients are positive and decrease slightly while in the partial autocorrelation functions the first coefficients have an high value but the other ones are not so different from zero. To verify non-stationary I can test for unit roots. There are a large number of options in EViews concerning the test and we can also choose among various methods and criteria
for determining the optimum lag length in the test. I opt for conducting (on the raw data series, with a constant but no trend in the test equation): the Augmented Dickey-Fuller (ADF) test, with up to 12 lags of the dependent variable, leaving the default Schwarz Info Criterion to select the optimum length; the Phillips-Perron (PP) test and the Kwaitkowski-Phillips-Schmidt-Shin (KPSS) test, with the default spectral estimation method of Bartlett kernel and the Newey-West bandwidth. The results are as follows:
In the ADF test we can see that the SIC selects 1 lag as optimum length for all the three series; the tStatistic values let us not to reject the null hypothesis of a unit root in all the cases since they are all not more negative than the test critical values at 1%, 5% and 10% level. In the PP test we have that the adopted bandwidth is different for each series: 2 for UK Pound Sterling, 5 for US Dollar and 3 for Japanese Yen. However, in every case, the adjusted t-Statistic values are not more negative than all the test critical values, so we do not reject the null hypothesis of a unit root again. Finally, the KPSS test (with a bandwidth of 11 for all the series) leads to reject the null of stationary for UK Pound Sterling and US Dollar only. The Japanese Yen LM-Statistic value is less than the asymptotic critical values at 1%, 5% and 10% level, so in this case we cannot reject the null hypothesis of stationary. Considering the tests for unit root's results, it could be necessary to differentiate the three series in order to obtain the stationary. I repeat the ADF test, the PP test and the KPSS test with the same characteristics described above, but this time I conduct them on the first differences of the data.
In this case we reject the null hypothesis of a unit root for all the three series in both the ADF test and the PP test. The KPSS test leads to the non-rejection of the null hypothesis of stationary regarding UK Pound Sterling and US Dollar, but it leads to the rejection of the null in the Japanese Yen case. So, according to the ADF and PP tests, the series are all non-stationary. The KPSS test agrees with them except concerning the Japanese Yen. Non-stationary of the data means that the persistence of shocks will always be infinite, standard regression techniques cannot be applied (the result could be a "spurious model�), the standard assumptions for asymptotic analysis are not valid and it is not possible to validly undertake hypothesis tests about the regression parameters. 2.3.
The data, therefore, contain a unit root and are thus I(1) (that is the series must be differenced once before they become stationary, then they are said to be integrated of order 1). Now, if a linear combination of these variables is stationary, the set of them is defined cointegrated. In this case I should test for cointegration because pure first difference models have no long-run solution. Made sure that the variables are all non-stationary in their levels form, I can proceed in the cointegration test within the Johansen VAR framework. I choose to examine the sensitivity of the result to the type of the specification used among the possible options by selecting to summarize all the five sets of allowed assumptions. The upper part of the results (johansen_cointegration) is the following:
Date: 01/03/16 Time: 16:19 Sample: 1999M01 2015M11 Included observations: 190 Series: JAPANESE_YEN UK_POUND_STERLING US_DOLLAR Lags interval: 1 to 12 Selected (0.05 level*) Number of Cointegrating Relations by Model Data Trend: Test Type Trace Max-Eig
None None Linear Linear Quadratic No Intercept Intercept Intercept Intercept Intercept No Trend No TrendNo Trend Trend Trend 0 0 0 0 0 0 0 0 0 0
*Critical values based on MacKinnon-Haug-Michelis (1999)
The results across the five types of model and the types of test (Trace and Max-Eig tests) agree upon the non-cointegration between the vectors. 2.4.
Testing for non-linearity
Financial theory suggests that there could be a non-linear relationship between the variables concerning exchange rates series. In particular, exchange rates are characterized by volatility clustering: the tendency for volatility to appear in bunches, that is large changes are expected to follow large changes and small changes to follow small changes. There can be also the probability of leverage effects: the tendency for volatility to rise more following a large fall then following a rise of the same strength. Linear structural models are unable to explain all these features. First of all I should testing for non-linearity on statistical grounds. I can start running a Brock-DechertScheinkman (BDS) independence test on the raw data to see if there is anything there. I opt to specify the distance as a fraction of pairs (the default option since this method is the most invariant to different distributions of the underlying series); the value used in calculating the distance in the amount of 0.7, that is a good starting point for the default method; the maximum correlation dimension is set equal to 12 and I choose not to use bootstrapping. The test has as its null hypothesis that the data are pure noise. The p-values associated to the BDS-Statistics for all the three series lead to the rejection of the null hypothesis.
The rejection of the null hypothesis implies that there is remaining structure in the series which could include hidden non-linearity, hidden non-stationary or other types of structure missed. To examine in depth the presence of non-linearity in the data I can test for ARCH effects. The test is calculated by regressing the squared residuals on a constant and 12 lags (the number of lags to include is chosen arbitrarily). I first estimate a linear model: I opt for an AR(2) considering acf and pacf structure and series identification (jap_yen_ar2, uk_pound_ster_ar2, us_dollar_ar2). Then I run a Heteroskedasticity test on the residuals, selecting the ARCH test type. Regarding the outputs, both the F-version and the LM-Statistic are very significant for UK Pound Sterling and US Dollar. This suggests the presence of ARCH effects in Pound and Dollar data. Observing the test results for Japanese Yen, instead, we cannot reject the null hypothesis of homoskedasticity.
Model selection
Until here we observe, first of all, that the series are not normally distributed and they are characterized by a robust volatility in their trend. From the autocorrelation and partial autocorrelation functions we can exclude ARMA and ARIMA models because the three exchange
rates series have not a geometrically decaying pacf. At this point the AR(2) could seem the most appropriate model to fit the data (since there is a geometrically decaying acf and a number of non zero points of pacf equal to 2, that determine the AR order). Financial theory should not suggest that there could be a two-way relationship between more exchange rates series, so we do not consider simultaneous equations systems like 2SLS and IV methods. Going on with the analysis, we observe that the series are not-stationary but not cointegrated so it does not make sense using a VAR or a VECM model. Furthermore, the presence of non-linearity lead us to test the presence of ARCH effects with the rejection of the null hypothesis of homoskedasticity. All these results make us to estimate a non-linear model of the GARCH family.
3. ARCH-GARCH model5 The non-linear ARCH (autoregressive conditionally heteroskedastic) model let us to deal with heteroskedasticity. It is unlikely that in financial time series the variance of the error will be constant over time, and hence it makes sense to consider a model that does not assume that the variance is constant and which describes how the variance of the errors evolves. Furthermore, it can capture volatility clustering for which the current level of volatility tends to be correlated with its level during the immediately previous period. Obviously, this model is used only for UK Pound Sterling and US Dollar data, since it is not appropriate in order to describe Japanese Yen series. A more widely employed model is the GARCH (generalized autoregressive conditionally heteroskedastic) model, because it is more parsimonious and avoids overfitting, considering also that a GARCH(1,1) model can be written as a restricted infinite order ARCH model. To estimate the model it is necessary to specify both the mean and the variance equations. I enter the dependent variable and the constant term C for the mean equation and I leave the default option for the variance equation. I need also to specify the number of ARCH and GARCH terms. For UK Pound Sterling both are set equal to one, that is one lag for the squared errors and one lag for the conditional variance (a GARCH(1,1) model). For US Dollar the ARCH term is set equal to one while the GARCH term equal to zero (a GARCH(0,1) model). These choices are adopted looking at the minimization of the Information Criteria's values and the maximization of the log-likelihood. Among the available options in EViews, I check the Heteroskedasticity Consistent Covariance only for UK Pound Sterling, since I suspect that the residuals could not be conditionally normally distributed. This option compute the quasi-maximum likelihood, covariances and standard errors using the method described by Bollerslev and Wooldridge and only the estimated covariance matrix will be altered (the parameters estimates will be unchanged). For both, the selective iterative method to achieve the convergence is the Marquardt algorithm. These are the results (uk_pound_sterl_garch11 and us_dollar_garch01):
Dependent Variable: UK_POUND_STERLING Method: ML - ARCH (Marquardt) - Normal distribution Date: 01/04/16 Time: 14:27 Sample: 1999M01 2015M11 Included observations: 203 Convergence achieved after 48 iterations Bollerslev-Wooldridge robust standard errors & covariance Presample variance: backcast (parameter = 0.7) GARCH = C(2) + C(3)*RESID(-1)^2 + C(4)*GARCH(-1)
Page "garch_model� in the EViews workfile.
Std. Error
3.095987 11.21413 -1.682333
0.0020 0.0000 0.0925
Variance Equation C RESID(-1)^2 GARCH(-1) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.
0.738084 0.093908 -3.060069 -2.994784 -3.033657
Dependent Variable: US_DOLLAR Method: ML - ARCH (Marquardt) - Normal distribution Date: 01/04/16 Time: 15:06 Sample: 1999M01 2015M11 Included observations: 203 Convergence achieved after 53 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(2) + C(3)*RESID(-1)^2 Variable
Std. Error
4.055111 3.064007
0.0001 0.0022
Variance Equation C RESID(-1)^2 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
0.000670 0.972242 -0.252734 -0.252734 0.200397 8.112141 171.3859 0.022530
0.000165 0.317311
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.
1.218957 0.179045 -1.658974 -1.610011 -1.639166
The outputs are divided into two sections: the upper part provides the standard output for the mean equation, the lower part contains the coefficients, standard errors, z-Statistics and p-values for the coefficients of the variance equation. Starting from the UK Pound Sterling, in the conditional variance equation, the coefficient on the lag squared residual is highly significant but the one on the lagged conditional variance is not. As is typical of GARCH model estimates, the sum of the coefficients on the lagged squared error and lagged conditional variance is very close to unity (approximately 1.02), indicating that volatility shocks are quite persistent. The variance intercept term is very small, the ARCH parameter is grater then 1 and the GARCH parameter has a negative sign. The bottom panel of the output presents the standard set of regression statistics using the
residuals from the mean equation. In this case measures as R² could be meaningless because there are no regressors in the mean equation (here R² is negative). Finally, in this case the log-likelihood could be not appropriate because we have used the heteroskedasticity consistent covariance option in the GARCH estimation, which calculate the quasi-maximum likelihood (QML). What is about the GARCH(0,1) model on US Dollar series, we have a statistically significant ARCH term (which is close to unity even if its standard error is not too low) and a small variance intercept. Here, the sign of the parameters are as expected. In this case the convergence is achieved after more iterations than the previous GARCH model (53 iterations against 48 for UK Pound Sterling model). Now, it could be useful to analyze the GJR model for possible asymmetries, the EGARCH model to avoid non-negativity constraints of the conditional variance and the GARCH-M model to take account of risk's influence and then to compare all these variations with the models estimated above. To estimate the GJR model for US dollar we need to change the threshold order number from 0 to 1 in the main menu screen for GARCH estimation. To estimate an EGARCH model, indeed, we need to select the EGARCH model estimation method. For US Dollar (us_dollar_gjr and us_dollar_egarch), the asymmetry terms in both models (the GJR(0,1) and the EGARCH(0,1)) are not statistically significant and furthermore the information criteria are bigger than the previous GARCH model ones. The GARCH-M(0,1) model (us_dollar_garchm), with the conditional logarithm of the variance term in the mean, shows that the estimated parameter on the mean equation has a negative sign and it is highly statistically significant. Information criteria too are smaller than above (more negative) and the log-likelihood is bigger. Thus we can conclude that, for this effective exchange rate series, there is feedback from the logarithm of the conditional variance to the conditional mean. The ARCH term in the variance equation has a positive sign, it is close to unity and it is still highly significant.
Talking about the UK Pound Sterling series, I remake all the passages described above, estimating a GJR(1,1) model, an EGARCH(1,1) model and a GARCH-M(1,1) model with the conditional logarithm of the variance term in the mean (uk_pound_sterl_gjr, uk_pound_sterl_egarch and uk_pound_sterl_garchm). We can observe almost the same tendency seen for US Dollar's models: in the GJR and EGARCH models, the asymmetry terms are not significant and both the models are less appropriate according to information criteria. On the other hand we have a log(GARCH) term in the mean equation that is very significant even if it is quite small. Here it appears with a positive sign as well as both the ARCH and GARCH terms in the variance equation. The last two coefficients are significant again. This model appears to capture data's features better than the first one.
We can conclude that there is some feedback from the conditional variance to the conditional mean, specially for US Dollar model, where increased risk (given by an increase in the conditional variance) leads to a decrease in the mean, that is a reduction of the relative strength of the currency against Euro. In the UK Pound Sterling model, the conditional logarithm of the variance has a too small value to consider a relevant influence of the conditional variance on the conditional mean.
4. Diagnostic checking6 Once our models have been estimated, I proceed to perform some diagnostic tests in order to verify the plausibility of the hypothesis that make up the models' basis. 4.1.
The followings are the representations for UK Pound Sterling's GARCH-M(1,1) and US Dollar's GARCH-M(0,1) respectively, available in the views: Estimation Command: ========================= ARCH(ARCHM=LOG,H,DERIV=AA) UK_POUND_STERLING C Estimation Equation: ========================= UK_POUND_STERLING = C(1)*LOG(GARCH) + C(2) GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) Substituted Coefficients: ========================= UK_POUND_STERLING = 0.00992398696085*LOG(GARCH) + 0.768281354478 GARCH = 3.4215421254e-05 + 1.01066713411*RESID(-1)^2 + 0.144122942586*GARCH(-1)
Estimation Command: ========================= ARCH(1,0,ARCHM=LOG,DERIV=AA) US_DOLLAR C Estimation Equation: ========================= US_DOLLAR = C(1)*LOG(GARCH) + C(2) GARCH = C(3) + C(4)*RESID(-1)^2 Substituted Coefficients: ========================= US_DOLLAR = -0.0143569236623*LOG(GARCH) + 1.21697942983 GARCH = 0.000694250034735 + 0.964028322033*RESID(-1)^2
The two representations display the estimation command as well as the estimation equation and the substituted coefficients equation for the mean and variance specification. Summarizing, for UK Pound Sterling series we have a GARCH-M(1,1) model in which the logarithm of the conditional variance enters in the mean equation multiplied for a low coefficient (added to the constant). In the variance equation we have a small constant, the ARCH term, approximately equal to one, that multiplies the square of the residuals of the previous period, and the GARCH term, still positive, for
Page "garch_model" in the EViews workfile.
the one lag of the conditional variance. The ARCH term measures the volatility observed in the previous period whereas the GARCH term measures the forecasted variance from the last period. For US Dollar series we have a GARCH-M(0,1) model with the constant and the logarithm of the conditional variance in the mean equation too. In the variance equation, here, we have the constant and the ARCH term only. So this model depends entirely on the influence of the volatility of the previous period that has a high, positive coefficient. 4.2.
Residual graphs
Here the graphs of residual, actual and fitted errors of both models (uk_poundsterl_residgraph and us_dollar_residgraph): 1 UK Pound Sterling 1.0 0.9 0.8 .2
0.7 0.6
-.2 2000
2008 Actual
2 US Dollar 1.6
1.4 .4 1.2 .2 1.0 .0 0.8 -.2
-.4 2000
2008 Actual
2010 Fitted
As we can observe, the errors fitted by the models are much less volatile than the actual errors. The difference between the actual and fitted errors (the blue line) stands around zero throughout the period 2003-2007 for the UK Pound Sterling and in some points only for the US Dollar. So it shows that they are far away from the real trends. 4.3.
Among the residual diagnostic tests we have the Correlogram-Q-Statistic that shows the correlogram (autocorrelations and partial autocorrelations) of the standardized residuals. It is used to test remaining serial correlation in the mean equation and to check the specification of the mean equation (uk_poundsterl_correlogram and us_dollar_correlogram). If the mean equation is well specified, all Q-Statistics should not be significant.
Unfortunately, in both case all the coefficients are high significant till the 12th lag. This means that something is missed in the specification of the mean equation. Through the correlogram of the squared standardized residuals I can test for remaining ARCH effects in the variance equation and to check its specification. As above, if the variance equation is correctly specified, all the Q-Statistics should not be significant (uk_poundsterl_resid2corr and us_dollar_resid2corr).
For the UK Pound Sterling model all the coefficient are not significant so we do not reject the null hypothesis of no autocorrelation. This let us to affirm that, in this case, the variance equation is well specified. About the US Dollar model, only the coefficient on the third lag is not significant. All the others p-values are less than 0.05 but not less than 0.01 so they are significant but not high statistically significant. Looking back to the estimation outputs in fact, we observe that the DurbinWatson values are very close to zero (approximately 0.02 and 0.03 for UK Pound Sterling and US Dollar respectively), that is there is positive autocorrelation in the residuals and so something is missed in the specification of the variance equation. 4.4.
Remaining ARCH effects
However, according to the ARCH LM residual diagnostic test (run with 12 lags), in both models we do not reject the null hypothesis of homoskedasticity in the variance equations, as we can see below (uk_poundsterl_archlm and us_dollar_archlm):
The p-values associated to both the F-version and the χ²-version are greater than 0.05 and so the statistic values are not significant. After this we can conclude that there are no ARCH effects left in the standardized residuals and the variance equation could be correctly specified. 4.5.
Testing for normality
Moving to the histogram of the normality test over the standardized residuals, we see a nonnormality behavior in the data (uk_poundsterl_stat and us_dollar_stat): the kurtosis value is less than 3 and the skewness is positive for US Dollar and negative for UK Pound Sterling. The JarqueBera test, effectively, leads to the rejection of the null hypothesis of normality. 3 UK Pound Sterling 36
Series: Standardized Residuals Sample 1999M01 2015M11 Observations 203
32 28
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
24 20 16 12 8
Jarque-Bera Probability
0.350594 0.796137 1.873206 -1.283871 0.856994 -0.534607 1.850069 20.85454 0.000030
0 -1.0
4 US Dollar 50
Series: Standardized Residuals Sample 1999M01 2015M11 Observations 203
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
-0.280671 -0.757638 2.585972 -1.889315 0.962926 0.652646 2.136828
Jarque-Bera Probability
20.71320 0.000032
0 -2.0
We must recall that the conditional normality assumption for the standardized residuals is essential for the maximization of the likelihood function. The upshot is that these GARCH-M models are
able to capture some although not all the features in the unconditional distributions of the effective exchange rates. However, even if the conditional normality assumption does not hold, the parameter estimates are still consistent if the equations for mean and variance are correctly specified, but usual standard error estimates are inappropriate.
5. Forecasting7 Last but not least, I proceed to estimate forecasting. Forecasting is an attempt to determine the values that a series is likely to take. Determining the forecasting accuracy of a model is an important test of its adequacy. Consider that, sometimes, even if a model violates the basic assumptions or contains insignificant parameters, it is affirmed to be irrelevant if the model produces accurate forecast. The dynamic forecast plot (to calculate multi-step forecasts starting from the first period in the forecast sample), produced for US Dollar over the whole sample (us_dollar_df), is given below: 1.8
Forecast: US_DOLLAR_DFORECAST Actual: US_DOLLAR Forecast sample: 1999M01 2015M11 Included observations: 203 Root Mean Squared Error 0.185616 Mean Absolute Error 0.142740 Mean Abs. Percent Error 13.19223 Theil Inequality Coefficient 0.074106 Bias Proportion 0.084012 Variance Proportion 0.911304 Covariance Proportion 0.004684
0.8 00
.030 .028 .026 .024 .022 .020 .018 00
Forecast of Variance
The dynamic forecast shows a completely flat forecast structure for the mean because of the formulation of the conditional mean equation. The forecast of variance provides the basis of the standard error bands that are given by the dotted red lines around the conditional mean forecast. Since the conditional variance forecasts decrease rapidly as the forecast horizon increases, the standard error bands shrink slightly. In the box on the right we have the forecast evaluation statistics for the conditional mean. Here we find some useful information like the decomposition of the
Page "forecasting" in the EViews workfile.
forecast error: the mean squared forecast error can be decomposed into a bias proportion, a variance proportion and a covariance proportion. The bias proportion measures the extent to which the mean of the forecasts is different from the mean of the actual data (whether the forecasting is biased). The variance component measures the difference between the variation of the forecasts and the variation of the actual data. The covariance component captures any remaining unsystematic part of the forecast errors. Accurate forecasts should be unbiased and also have a small variance proportion, so that most of the forecasting error should be attributable to the covariance. In this case, unfortunately, most of the forecast error is caused by the difference between the variation of the forecasts and the variation of the actual data (the variance proportion is close to unity). In any case, the root mean squared error and the mean absolute error are low enough. The static forecast plot (to calculate a sequence of one-step-ahead forecasts, rolling the sample forwards one observation after each forecast to use actual rather than forecasted values for lagged dependent variables) for US Dollar over the whole sample is the following (us_dollar_sf): 2.4
Forecast: US_DOLLAR_SFORECAST Actual: US_DOLLAR Forecast sample: 1999M01 2015M11 Included observations: 203 Root Mean Squared Error 0.178259 Mean Absolute Error 0.136437 Mean Abs. Percent Error 12.59687 Theil Inequality Coefficient 0.070871 Bias Proportion 0.129306 Variance Proportion 0.748661 Covariance Proportion 0.122033
0.4 00
Âą 2 S.E.
.00 00
Forecast of Variance
The static forecast shows a fall of the variance forecast even if these graphs are characterized by much more volatility. This also results in more variability in the standard error bars around the conditional mean forecast. Nevertheless, the forecast of the mean lies always inside the confidence interval. The root mean squared error, the mean absolute error and the mean absolute percent error
too are all less than the ones in the dynamic forecast. But in this case the forecast is a little more biased than above, even if the variance proportion is lightly decreased and a smaller portion of the forecast error is attributable to the covariance portion. Here the results for UK Pound Sterling model (dynamic and static forecast over the whole sample): 8,000,000
Forecast: UK_POUNDSTERL_DFORECAST Actual: UK_POUND_STERLING Forecast sample: 1999M01 2015M11 Included observations: 203 Root Mean Squared Error 0.126522 Mean Absolute Error 0.112833 Mean Abs. Percent Error 15.92663 Theil Inequality Coefficient 0.079218 Bias Proportion 0.768723 Variance Proportion 0.006122 Covariance Proportion 0.225156
-8,000,000 00
Âą 2 S.E.
0 00
Forecast of Variance
The dynamic forecast (uk_pounsterl_df) shows a completely flat forecast structure for the mean, as well as the US Dollar's one. The forecast of variance is flat too and rises only at the end of the sample. This leads to straight error bars that lie on the conditional mean to widen, then, at the end of the sample. In the right box we can see how the most of the forecast error is caused by the bias proportion while the variance proportion is almost zero. This means that we have a large difference of the mean of the forecasts from the mean of the actual data, even if the mean absolute error and the root mean squared error are small.
Forecast: UK_POUNDSTERL_SFORECAST Actual: UK_POUND_STERLING Forecast sample: 1999M01 2015M11 Included observations: 203 Root Mean Squared Error 0.090316 Mean Absolute Error 0.073253 Mean Abs. Percent Error 9.659205 Theil Inequality Coefficient 0.062102 Bias Proportion 0.096759 Variance Proportion 0.636225 Covariance Proportion 0.267016
0.2 00
Âą 2 S.E.
.00 00
Forecast of Variance
The static forecast (uk_poudsterl_sf) of the variance shows a relevant volatility and a flattening at the middle of the sample. This also results in more variability in the standard error bars around the conditional mean that get close to the mean in the middle part of the sample. In this case the root mean squared error, the mean absolute error and the mean absolute percent error too are greater than the ones in the dynamic forecast. But here the error forecast is mostly due to the variance proportion while the covariance proportion is almost equal. The latter forecast is less biased than the former.
We can estimate forecasting for all the sub-samples we desire. To make an example, these are the dynamic and static forecasts for both series over the last two years (us_dollar_df2, us_dollar_sf2, uk_poundsterl_df2 and uk_poundsterl_sf2):
Finally, even if we kept out the other variants of the GARCH model before, we could estimate forecasting for those models too and then to compare them with the selected model. This because some models can produce an accurate forecasting even if, in their estimation, they cannot capture all the data features. To make just an example, the GARCH(1,1) model for UK Pound Sterling (uk_forecast3) produces a more unbiased dynamic forecast (but we don't have the variance and the covariance proportion) with smaller mean errors:
Conclusions Summing up, I chose to analyze how the past volatility of a currency influenced its current value. To do this, I imported the monthly effective exchange rates of the currency as a measure of its relative strength against the Euro. The Japanese Yen, the US Dollar and the UK Pound Sterling showed themselves to be the three most traded currencies on the forex market, hence I opted to conduct the survey with these three series. All of them were characterized by an evident volatility, more highlighted during 2007-2008 (the years of the financial crisis) and during 2012-2013 for Japanese Yen only. For almost the first part of the sample, US Dollar and Japanese Yen followed a similar path. During 2008 and 2009 they started to differ from one another and in the second part of the sample we observed that US Dollar and UK Pound Sterling's trend were more similar, while Japanese Yen gradually detached. Furthermore all the three series were far away from the normal distribution: they were skewed and platykurtic, even if for the Yen we could not reject the null hypothesis of normality in the JarqueBera test. The three currencies had a geometrically decaying ACF and a number of non-zero PACF coefficients equal to 2. The results of the ADF test, the PP test and the KPSS test led to the rejection of the stationary null hypothesis for the Dollar and the Pound but not for the Yen. Since pure first differences models have no long-run solution, I tested for cointegration (within the Johansen VAR framework), non-linearity (with the BDS independence test) and for ARCH effects. The series were not cointegrated but they had not a linear structure and there were the presence of ARCH effects in the US Dollar and UK Pound Sterling data. For this reason any linear model could not be estimated and hence the GARCH model appeared the most appropriate. Following the maximization of the log-likelihood function and the minimization of the information criteria as measures to compare the variations of the GARCH family models, I estimated a GARCH-M(0,1) for the Dollar and a GARCH-M(1,1) for the Pound, since there were visible feedback from the logarithm of the conditional variance to the conditional mean. Even if I succeeded in removing ARCH effects, after the estimation of the models there were still some problems. The fitted residuals showed lesser volatility but they were autocorrelated and they did not follow a normal distribution. This could be attributed to a non-correctly specified mean equation for both the US Dollar and the UK Pound Sterling. Forecasting accuracy was medial: both models produced forecastings with low mean errors but a bit biased or with a large variance proportion which determined the forecast error.
The conclusion is that these GARCH-M models are able to capture some although not all of the features in the unconditional distributions of the series. These models could be improved, maybe, with a better specified mean equation, considering the real effective exchange rates instead of the nominal ones, providing with seasonal adjustment or a better structured sample, added to solid theoretical grounds. Hence, we can consider this report as a starting point for a more valid and accurate analysis.


text:
Apple looks to buy Microsoft Kinect company PrimeSense?
Apple is negotiating to acquire PrimeSense, the company that created the technology behind Microsoft's Kinect, according to a report from Israeli publication Calcalist. Though the negotiations are in the early stages, a deal could reportedly cost Apple around $280 million. PrimeSense holds important patents related to motion sensors reading body movements, which Apple may want to use as it develops its next-generation Apple TV and/or television set. Microsoft used the company's technology for Kinect, and is now using it in the enhanced Kinect hardware developed for Xbox One. [via 9to5Mac]
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text:
|Reviews for Travelin' Soldier
| I.Am.Lucy.Heartfilia NaLu chapter 1 . 5/28/2016
sequel please!
| Dpressed Angel chapter 1 . 7/15/2011
I cried! Oh My Gosh! I cried! I. LOVED! It. You should write a sequil.
| Tywell chapter 1 . 7/11/2005
girl all i can say is that you scared me for a second because I acualy thought he was dead. I also didn't know that the lyrics went that way. I almost cryed. This was really great and keep writing!
| Black-Moon-Goddess chapter 1 . 6/23/2005
*Screams: OMFG! I LOVE YOU! While chocking you to death* How could you make me cry like that when you changed it to be a GOOD ending? When I first heard the song, I didn't know what it ment, until my dad explained the soldier had died, and that made me cry. But I'm happy now, becuase Inuyasha's coming home.
(And I couldn't help singing along to the lyrics. -_- I love DC.) *adds this story to favourites, too*
| kenya chapter 1 . 6/5/2005
im not one for song fic's (just never cared for em) but that was pretty damn good!
| orangepencils chapter 1 . 5/21/2005
OMG! That was so I don't know what to call it. I'm like in tears right now but that's a detail. It was really a cute story. I don't know the song but it was just so perfect...Really nice story!
| Nyamh Rosie chapter 1 . 12/17/2004
great and emotional story
| CircadianDance chapter 1 . 8/6/2004
That was really good! -wipes eyes- I wasn't about to cry...really...
| DarkAngel075 chapter 1 . 5/31/2004
I love it so much!
| punkkagome chapter 1 . 5/1/2004
Believe it or not. I actually like that song, my mom and dad like DC, so I hear it fairly often in the car or whatnot. my two best friends are in the Army, Jeremy is a combat medic, and Jason is intelligence.. Jeremy's in Iraq, noone knows when he's going to get out.. My ex boyfriend (still a very good friend) was a Marine, and as far as I know, he's gone to Iraq as well.. Even though he got out a few months before they began send them out there. But i like the fic. It was great. *wipes the little tears away*
| Artcat411 chapter 1 . 12/4/2003
YAY! GOOD STORY! -y i luv the song, but hate how the soldier dies! great ending!
PS. Is this a oneshot? if not plz update!-
| mika chapter 1 . 12/3/2003
that was one of the few dixie chicks songs i liked. and you did an awesome job writing a fic to that song.
| Nibzo chapter 1 . 12/3/2003
oh, this is a cool fic. It reminds me of the movie i once watched, The Last Bridge Home. But, thats a kinda different story, i have no room for you on my fav. stories, so i'll puit you on my Fav. authors


text:
Mr. Yung joined Monterey Mills in April 2014. He is an accomplished sales and marketing executive with over 30 years sales and marketing experience working for Oster/Sunbeam, Mr. Coffee, Inc., Mr. Coffee Concepts, Seiko Corporation of America and Brew1 Coffee Company. Dan has successfully built two start-up companies and industry recognized for creating the Hospitality guest amenity of "In-Room Coffee" offered today in over one million hotel rooms worldwide. He is a results-oriented, decisive leader with proven success in developing substantial business growth, new business development and profit improvement.
Mr. Yung is directly responsible for all sales and marketing of non-paint roller business for Monterey Mills and its various business channels/brands (Denali Home Furnishing Collection – Palace Pet – Glenoit Fabrics). Mr. Yung is a member of the company's strategy team.
Mr. Yung holds a Bachelor of Business & Marketing from The University of Akron and Associate Degree of Criminal Justice from The University of Akron.
In his role, Mr. Ellison is directly responsible for day-to-day production and management of union employees, non-union supervisors, and managers. He has brought production to levels never before achieved, improved quality standards and has expanded product lines into new markets by introducing new production techniques.
In his current role, Mr. Lowder is responsible for the Paint Applicator accounts. He coordinates developments between the R & D staff and customers to address constantly changing market demands. In addition, he works with the Purchasing team to address the dynamic raw material market to insure best product and pricing is offered. Mr. Lowder also works with the manufacturing group to address customer demands and promotions that must be met in order to successfully service today'sretail market.
Mr. Lowder has a Bachelor of Arts in Accounting from North Carolina State University.
Mr. Janke joined Monterey Mills' accounting team in April 2000. Prior to joining Monterey Mills, Mr. Janke worked for J.G. Van Holten and Sons, Inc. as Controller, directing all aspects of financial reporting, accounting, IT and personnel. Mr. Janke brings more than 35 years of experience in accounting for manufacturing entities and has a strong cost accounting background.
Mr. Janke is directly responsible for all aspects of finance and accounting at Monterey Mills and Glenoit Fabrics. He holds a Bachelor of Business Administration with a major in Accounting from the University of Wisconsin in Milwaukee, and a Master of Business Administration from Keller Graduate School of Management. He is a Certified Public Accountant and is a member of the Wisconsin Institute of Certified Public Accounts.


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Web-enabling technologies represent the next generation of design environments to design and manufacture complex systems, such as them characterizing contract furniture. In the context of web applications to facilitate and support teamwork in collaborative product development, the paper presents a CAD-based infrastructure for the 3D visualization of… (More)
- Margherita Peruzzini, Maura Mengoni, Damiano Raponi
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Numerous companies all around the world are shifting from traditional products to product-service solutions, thanks to the increased "intelligence" and " connectivity" of modern products and the more deep integration among mechanics, electronics, Information and Commutation Technologies (ICT) and Internet of Things (IoT). Such… (More)
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Quality of life of various types of people can strongly benefit of a design process developed to take into account needs and requirements of end users. In this context the paper present a study on the cognitive and physical abilities of elderly persons, to design a friendly kitchen, that is considered one of the most complex home environment for the… (More)
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|Reviews for Facts and Assumptions
| The Evil Ninja chapter 2 . 6/12/2013
Again, as with Embarassed Meetings, where's the next chapter? I am curious about how both stories end!
| corruptedweirdo101 chapter 2 . 12/28/2010
Hi your story is awesome! and yes i was thinking sick. lol :)
| writing.in.a.tree chapter 2 . 1/11/2008
i love it! its hilarious! please please please PLEASE update!
| writing.in.a.tree chapter 1 . 12/31/2007
I loved it !absolutely hilarious and wonderful! Your writing is so good...You made it worth it for me to be sick with the chickenpox on christmas break, because no one is bugging me to stop reading your stories on the computer, or laughing my brains out. Great sense of humor!
| Yorai chapter 2 . 11/4/2007
haha what a perfect example to show how the pervert mind of people can make us misunderstand the whole situation.
| deshielover chapter 2 . 9/20/2007
I really like it...its hilarious...you should update soon. but no pressure.
| SidekickKep chapter 2 . 7/16/2007
Wow, this review is like over a year later and all... but whatever.
This was just so hilarious! I mean, I just couldn't stop laughing. I mean, WHO!
And I dunno if you abandoned this or not, but please don't! I need to know what happens next!
The review was short, I know, but I'm rushing; I gotta be somewhere...
| Rizasfirstlove chapter 2 . 7/14/2007
aww but it wa so funny.
| Kurissyma san Tybalt chapter 2 . 6/3/2007
Hehehe P Update soon!
| Kurissyma san Tybalt chapter 1 . 6/3/2007
It shall be funny to read the next chapter and find out whats's up, lol P I'm thinking target practise- or re-assembling guns or something P
| Danelle chapter 2 . 4/13/2007
You have a twisted is hilarious though.I can't wait for the next chapter!
| M.C.E.Black chapter 2 . 1/22/2007
Hahahahaha. This is so unbelievably amazing. The first chapter I was like: "What the heck is going on? What could they possibly be doing, because I know it's not what we think it is..." But this is so clever and so amazing. I laughed so hard. I can't wait for the next chapter.
| IdiotsApprentice chapter 2 . 12/9/2006
This is my first review and all so... yeah.
I absolutely loved this! It was very well written, correct grammer and insanely funny! Not a combination I see often so very well done! I hope you post the next chapter very soon.
| Unexpected Hopeless Romantic chapter 2 . 7/24/2006
my not so innocent mind!wahahaha..so that explains it..hilarious!i'm such a pervert..and I'm a girL!update!I await for one!please!
| Twin-Lance chapter 2 . 6/9/2006
It was quite touching how Riza is helping Roy with his gun problem. I'd forgotten all about the doctors. That little bit of emotion on Roy and then Riza's part shows just how wrong everyone in the hallway is about what is really going on. I do believe that little bit has actually made this story. Everyone else truly has no idea the emotional healing going on inside.


text:
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Title : Looking for a fresh work ? are you certain you can move the setting verify ? Word depend : 433 Summary: People find refused with regard to jobs every single day. some job seekers dont contain the right experience among others dont job interview properly. but theres another key reason why prospects dont find work gives , one which hiring firms hardly ever present to job seekers these people didnt move the setting verify.
Keywords: background verify , pre-employment verification , work lookup , occupation , work application
Article body : Companies take a close look in whom these people use these days. via higher protection concerns to a increase in negligent hiring lawsuits , firms be aware that negative hiring selections may cause all of them big problems. Thats the reason why more than 90% involving employers right now run criminal background checks about probable uses. During any background check , any hiring business has a great research your individual along with specialized history. these people verify your own schooling along with previous employment , confirm if you find legal activity within your previous , along with talk to your references. Some firms could even research your driving history , credit rating or previous medicine tests results. To chance a detailed background check , a business requirements some very specific details of your stuff. for instance , they need contact details to your previous employers along with the names of your respective previous managers. This form of thorough details are usually certainly not available on any cv , also one that's expertly well prepared. Therefore firms have prospects fill in a career application which is specifically made to get the details thats required to chance a complete background check. How you fill in any companys job application is actually straight tied to whether or not you receive the task. the truth is , more than 80% involving firms point out in which inacucuracy on the job application may take an applicant from concern.
When you fill in a career application , you would like to ensure that the information you have is actually complete and straightforward to ensure. nOnetheless , there are various situations that can create problems to have an consumer. Below are a few : What kind of contact details do you give an early company containing removed bankrupt ? If you labored for any business by having a short-term organization , that you listing because the company ? If you are fired from your previous work , when you get out off the application ? How you response these types of as well as other queries will make the real difference between a speedy along with prosperous background check and one in which crushes to a stop due to the fact details cant always be approved. hr in most cases extreme caution versus hiring someone whose details are challenging to study , has breaks or disparity , or is actually missing essential particulars. A prosperous work lookup takes a professional-looking cv , robust choosing skills , plus a can-do mindset. these tools provide in the door along with help you make a final minimize. But in the event you dont move the setting verify , you wont receive the work. Take time to organize the particular thorough details which will embark on your career application. Its one of the best steps you can take within your work lookup. local services west palm beach area
Published on Jun 5, 2012
dont find work gives , one which hiring firms hardly ever present to job seekers these people didnt


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- Apartments
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This Listing is No Longer Available on WestsideRentals!
1 Bedroom,
Los Angeles, CA
City/Area: Los Angeles / Hollywood, CA 90028
Listing Number:937493
Rent: $4,100
Listing Type: Standard Rental
Deposit: $1,000
Bedroom(s): 1 Bedroom
Available: Available Now!
Bathroom(s): 2 Baths
Furnished: No
Lease Type: One Year Minimum Lease
Pets: Will Consider Pet
Structure Type: Condos
Unit Details: Not Specified Unit in 63-Unit Building
Parking: 1-car Subterranean Parking
Square Footage: 1217
96 out of 100
Walker's Paradise
Soundscore: 66 Busy
Vehicle Noise: Busy
Airport Noise: Calm
Businesses: Busy
Condo in Hollywood
Listing Info1+2 UNIT AVAILABLE!
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- With 180-degree-plus views of Hollywood from each of the 63 residences and floor-to-ceiling glass with 13-foot, six-inch ceilings, your home at Sunset Vine Tower lets you live in a private atmosphere of light. Offered now with the flexibility of leasing and elite levels of appointments equal to those of the finest, new condominiums. * Gourmet kitchen. * Central heating and air. * Secured resident parking. * City and mountain views of the Hollywood sign, the hills beyond to the Santa Monica Mountains, and the Pacific Ocean. * Unique floorplates make each residence a corner home for maximum light and views. Upper floors have even more wraparound vistas. * 13-foot, 6-inch ceilings. * In-home washer/dryer.
Paid Utilities
- water & hot water & trash & gardener & pool service


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Grotpeter Trail is a 2.7 mile heavily trafficked loop trail located near Saint Louis, Missouri that features a river and is rated as moderate. The trail offers a number of activity options and is best used from March until October. Dogs are also able to use this trail but must be kept on leash.
dogs on leash
kid friendly
mountain biking
nature trips
trail running
horseback riding
wild flowers
Varied routes and the challenging terrain with numerous elevation changes make this a popular trail with all users. In addition to the main trail, five white connector trails, totaling 2.6 miles, are available to enhance, shorten or lengthen the users experience. From the trailhead, east of the park office, the trail crosses Kiefer Creek Road before climbing up and over a hillside. The trail then winds up another hill to the top of a long ridge. White connector trails 1, 2, 3, 4 and 5 can be used to bypass steep sections of the trail. After reaching the pinnacle, the perimeter trail follows the crest of the ridge before starting a long downhill back into the Kiefer Creek valley and back to the trailhead.
love the roller coaster section
Nice trail
Shyan W.
it's nice but not nearly as nice as Castlewood's other trails
Nice dog walk with hills to keep it interesting and provide a bit of a work out. Got up close to deer who just stood and watched us walk by. Main complaints: no views and lots of mosquitos on a day after storms, so bring your bug spray.
Nice trail for running, hiking, and biking. Plenty of options to increase your mileage or cut it short.


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London - A four-week-old baby has been attacked by a fox in his home in London, police said on Sunday.
The baby boy was admitted to a hospital on Wednesday after the fox injured one of his hands, Scotland Yard said. It didn't give details about how the fox entered the home or how serious the injury was, but British media said the fox bit one of the baby's fingers.
The boy was recovering after surgery, the reports said.
Reports of urban foxes attacking humans occur infrequently in Britain. In 2010, twin baby girls were reportedly mauled by a fox while sleeping in their cots in a London home.
Some have called for a cull of the animals, which are a common sight at night in quieter areas of the capital. Mayor Boris Johnson said more needs to be done to tackle what he called a growing problem.
"They may appear cuddly and romantic, but foxes are a pest and a menace, particularly in our cities," he said.
The Royal Society for the Protection of Animals said that it is "extremely unusual" for the animals to attack anyone. - Sapa-AP


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Boarding Schools for Boys in Tullahoma, Tennessee
Boarding Schools for Boys in Tullahoma, Tennessee…
|More about boarding schools in Tullahoma, Tennessee:
Tullahoma is a city in Coffee and Franklin counties in the south-central part of the U.S. state of Tennessee. The population was 18,579 at the 2010 census. It is the principal city of the Tullahoma micropolitan area, which consists of Coffee, Franklin, and Moore counties and is the largest micropolitan area in Tennessee.
You found Safe Harbor Academy when you searched for boarding schools for boys.