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Bayesian Vector Autoregressions: Applications
Abstract and Keywords
Bayesian vector autoregressions (BVARs) are standard multivariate autoregressive models routinely used in empirical macroeconomics and finance for structural analysis, forecasting, and scenario analysis in an ever-growing number of applications.
A preeminent field of application of BVARs is forecasting. BVARs with informative priors have often proved to be superior tools compared to standard frequentist/flat-prior VARs. In fact, VARs are highly parametrized autoregressive models, whose number of parameters grows with the square of the number of variables times the number of lags included. Prior information, in the form of prior distributions on the model parameters, helps in forming sharper posterior distributions of parameters, conditional on an observed sample. Hence, BVARs can be effective in reducing parameters uncertainty and improving forecast accuracy compared to standard frequentist/flat-prior VARs.
This feature in particular has favored the use of Bayesian techniques to address "big data" problems, in what is arguably one of the most active frontiers in the BVAR literature. Large-information BVARs have in fact proven to be valuable tools to handle empirical analysis in data-rich environments.
BVARs are also routinely employed to produce conditional forecasts and scenario analysis. Of particular interest for policy institutions, these applications permit evaluating "counterfactual" time evolution of the variables of interests conditional on a pre-determined path for some other variables, such as the path of interest rates over a certain horizon.
The "structural interpretation" of estimated VARs as the data generating process of the observed data requires the adoption of strict "identifying restrictions." From a Bayesian perspective, such restrictions can be seen as dogmatic prior beliefs about some regions of the parameter space that determine the contemporaneous interactions among variables and for which the data are uninformative. More generally, Bayesian techniques offer a framework for structural analysis through priors that incorporate uncertainty about the identifying assumptions themselves.
Keywords: Bayesian inference, vector autoregression models, BVAR, SVAR, forecasting
Bayesian vector autoregressions (BVARs) have been applied to an increasingly large number of empirical problems. This article reviews some of the most common applications of BVARs in macroeconomics and finance such as structural identification, forecasting, and scenario analysis. A companion article surveys Bayesian inference methods for vector autoregression models, commonly used priors for economic and financial variables, and introduces the notation used throughout this paper (Miranda-Agrippino & Ricco, 2018).
Forecasting has featured predominantly in the development of BVARs. In this context, BVARs with informative priors have often proved to be superior tools compared to standard frequentist/flat-prior VARs. VARs are highly parametrized autoregressive models, whose number of parameters grows with the square of the number of variables times the number of lags included. Given the limited length of standard macroeconomic datasets—that usually involve monthly, quarterly, or even annual observations—, such overparametrization makes the estimation of VARs impossible with standard (frequentist) techniques, already for relatively small sets of variables. This is known in the literature as the "curse of dimensionality." BVARs efficiently deal with the problem of over-parametrization through the use of prior information about the model coefficients. The general idea is to use informative priors that shrink the unrestricted model toward a parsimonious naïve benchmark, thereby reducing parameter uncertainty and improving forecast accuracy. Section "Forecasting with BVARs" discusses forecasting with BVARs, while section "Conditional Forecasts and Scenario Analysis" focuses on conditional forecast and scenario analysis.
Another important area of application is the study of causal relationships among economic variables with structural (B)VARs (Sims & Zha, 1998). It is common practice to present results from SVARs in the form of impulse response functions—that is, causal responses over time of a given variable of interest to an "identified" economic shock—together with bands that characterize the shape of the posterior distribution of the model (see Sims & Zha, 1999).1 Section "Structural VARs" reviews Bayesian techniques in SVARs.
The application of Bayesian techniques to "big data" problems is one of the most active frontiers in the BVAR literature. Indeed, because they can efficiently deal with parameters proliferation, large BVARs are valuable tools for handling empirical analysis in data-rich environments (Bańbura, Giannone, & Reichlin, 2010). Important applications in this case also concern forecasting and structural analysis, where large-information BVARs can efficiently address issues related to misspecification and non-fundamentalness. De Mol, Giannone, and Reichlin (2008) have discussed the connection between BVARs and factor models, another popular way to handle large datasets. We review large BVARs in section "Large Bayesian VARs."
Forecasting With BVARs
Reduced form Bayesian vector autoregressions, that are written as(1)
when estimated with informative priors over the autoregressive parameters {Ai,…Ap} and the variance-covariance of the errors Σ, usually outperform VARs estimated with frequentist techniques (or flat priors). Using the frequentist terminology, reasonably specified priors reduce estimated parameters variance and hence improve forecast accuracy, at the cost of the introduction of relatively small biases. From a more Bayesian perspective, the prior information that may not be apparent in short samples—as, for example, the long-run properties of economic variables captured by the Minnesota priors—helps in forming sharper posterior distributions for the VAR parameters, conditional on an observed sample (see, e.g., Todd, 1984, for an early treatment of forecasting with BVARs).
Bayesian Forecasting
The fundamental object in Bayesian forecasting is the posterior predictive density.2 That is, the distribution of future data points , conditional on past data y1–p:T. Choosing a particular forecast —for example, the mode or median of the predictive distribution, alongside appropriate probability intervals –, is essentially a decision problem, given a specified loss function . The Bayesian decision corresponds to choosing the forecast that minimizes the expected loss, conditional on past data(2)
For a given loss function, the solution to the minimization problem is a function of the data, that is, F(y1-p:T). For example, with quadratic loss function , the solution is the conditional expectation . The predictive density is given by(3)
where θ is the vector collecting all the VAR parameters, that is A and Σ, p(θ|y1–p:T) is the posterior distribution of the parameters, and p(yT+1:T+H|y1–p:T, θ) is the likelihood of future data. Eq. (3) highlights how Bayesian forecasts account for both the uncertainty related to future events via p(yT+1:T+H|y1–p:T, θ), and that related to parameters values via p(θ|y1–p:T).
The posterior predictive density for h > 1 is not given by any standard density function. However, if it is possible to sample directly from the posterior probability for the parameters, Eq. (3) provides an easy way to generate draws from this predictive density.
Algorithm 1:Sampling from the Posterior Predictive Density.
For s = 1,. . .,nsim:
1. Draw θ(s) from the posterior p(θ|y1–p:T).
2. Generate from the distribution of the errors and calculate recursively from the VAR equations with parameters A(s).
The set is a sample of independent draws from the joint predictive distribution.
Kadiyala and Karlsson (1993) analyzed the forecasting performance of different priors and find that those that induce correlation among the VAR coefficients, for example, the sums-of-coefficient priors (Doan et al., 1984) and the co-persistence prior (Sims, 1993), tend to do better.
Carriero et al. (2015a) conducted an extensive assessment of Bayesian VARs under different specifications and evaluate the relative merits of different model specifications and treatments of the data. In particular, starting from a benchmark VAR in levels and with NIW, sums-of-coefficients, and co-persistence priors, they evaluate (1) the effects of the optimal choice of the tightness hyperparameters; (2) of the lag length; (3) of the relative merits of modeling in levels or growth rates; (4) of direct, iterated, and pseudoiterated h-step-ahead forecasts; (5) the treatment of the error variance Σ; and (6) of cross-variable shrinkage . They find that in general simpler specifications tend to be effective.3
Bayesian Model Averaging and Prediction Pools
Bayesian analysis offers a straightforward way to deal with model uncertainty. Consider for instance the two competing models and with likelihood and prior probabilities , and respectively. Bayesian model averaging (BMA) obtains the marginalized (with respect to the models) predictive distribution as(4)
where is the prior probability assigned to model , and is the model's marginal likelihood. Eq. (4) can be extended to allow for M different models. This can be seen as a generalization of the predictive distribution in Eq. (3) where instead of conditioning on a single model, M different models are considered. BMA was introduced in economic forecasting by the seminal Geweke (1999), and its applications in the context of forecast combinations and pooling have been numerous. Notable extensions to BMA include the Linear Optimal Prediction Pools of Geweke and Amisano (2011, 2012) and the Dynamic Prediction Pools of Del Negro et al. (2016).4 Earlier reviews of BMA and forecast combinations are in Geweke and Whiteman (2006) and Timmermann (2006). The evolution of forecast density combinations is discussed in detail in Aastveit et al. (2018) in the chapter in this collection. We refer the reader to their paper for further details.
Conditional Forecasts and Scenario Analysis
Forecasts that condition on a specific path for one of the variables, such as, for example, a preferred path for the policy interest rate, are of particular interest to central banks. Early treatment of such forecasts, also referred to as scenario analysis, is in Doan, Litterman, and Sims (1984), who noted that a conditional forecast is equivalent to imposing restrictions on the disturbances uT+l,…,ut+H. Waggoner and Zha (2012) suggested a way to compute conditional forecasts that does not condition on specific parameters values (e.g., the posterior means) and produces minimum squared forecast errors conditional on the restrictions. Moreover, it yields posterior distributions for the parameters that are consistent with the constrained paths. Let(5)
denote the desired restrictions on the future path of some of the variables in yt. These can be rewritten as(6)
and Cj are the coefficients of the MA representation with(8)
Rearranging Eq. (6) as(9)
defining G = RC′ and , and noting that , one obtains the conditional distribution of uT+1:T+H as(10)
that can be used to draw from the predictive distribution. In order to ensure consistency of the posterior distribution with the restriction in Eq. (9), Waggoner and Zha (2012) suggested treating yT+1:T+H as latent variables and simulating the joint posterior of the parameters and the future observations using the following MCMC sampler.
Algorithm 2: MCMC Sampler for VAR with restrictions on yT+1:T+H.
Given restrictions as in Eq. (9), select starting values for A(0) and Σ(0) using e.g. simulation on historical data. For s = 1,…, nsim:
1. Draw uT+1:T+H from the distribution in Eq. (10) and recursively calculate
2. Augment y1–p:T with and draw A(s) and Σ(s) from the full conditional posteriors
using an appropriate sampling given the chosen VAR specification and priors.
3. Discard the parameters to obtain a draw from the joint predictive density consistent with the restrictions in Eq. (9).
Jarociński (2010) suggested an efficient way to sample uT+1:T+H that reduces the computational burden of the algorithm discussed above. An extension to this method is in Andersson et al. (2010), who restrict the forecasts yT+1:T+H to be in a specified region . This is a case of "soft" restrictions, as opposed to those in Eq. (9). Robertson et al. (2005) followed a different approach and propose exponential tilting as a way to enforce moment conditions on the path of future yt. This is the approach also implemented in Cogley et al. (2005). These methods are typically used in conjunction with small VARs and become quickly computationally cumbersome as the system's dimension increases.
Bańbura et al. (2015) proposed instead a Kalman Filter–based algorithm to produce conditional forecasts in large systems that admit a state-space representation such as large Bayesian VARs and factor models. Intuitively, this method improves on computational efficiency due to the recursive nature of filtering techniques that allow to tackle the problem period by period.
Antolin-Diaz et al. (2018) proposed a method to conduct "structural scenario analysis" that can be supported by economic interpretation by choosing which structural shock is responsible for the conditioning path.
Structural VARs
Reduced form VARs can capture the autocovariance properties of multiple time series. However, their "structural interpretation" as the data generating process of the observed data, and of their one-step-ahead forecast errors in terms of economically meaningful shocks, requires additional identifying restrictions.
A VAR in structural form (SVAR) can be written as(11)
where B0 is a matrix of contemporaneous (causal) relationships among the variables, and et is a vector of structural shocks that are mutually uncorrelated and have an economic interpretation. All structural shocks are generally assumed to be of unitary variance. This does not imply a loss of generality, however, since the diagonal elements of B0 are unrestricted. In the structural representation, the coefficients have a direct behavioral interpretation, and it is possible to provide a causal assessment of the effects of economic shocks on variables—for example, the effect of a monetary policy shock on prices and output. Pre-multiplying the SVAR in Eq. (11) by B0–1 yields its reduced-form representation: that is, the VAR in Eq. (1). Comparing the two representations one obtains that Ai = B0–1Bi, i = 1,…,p, and ut = B0–1et. The variance of the reduced form forecast errors, ut is(12)
Since Σ is symmetric, it has only n(n + 1)/2 independent parameters. This implies that the data can provide information to uniquely identify only n(n + 1)/2 out of the n2 parameters in B0. In fact, given a positive definite matrix Σ, it is possible to write B0 as the product of the unique lower triangular Cholesky factor of Σ (Σ = ΣChol Σ′Chol) times an orthogonal matrix Q(13)
From this decomposition it is clear that while ΣChol is uniquely determined for a given Σ, the n(n – 1)/2 unrestricted parameters span the space of the O(n) group of n × n orthogonal matrices. The central question in structural identification is how to recover the elements of B0 given the variance-covariance matrix of the one-step-ahead forecast errors, Σ. That is, how to choose Q out of the many possible n-dimensional orthogonal matrices.5
From a Bayesian perspective, the issue is that since yt depends only on Σ and not on its specific factorization, the conditional distribution of the parameter Q does not get updated by the information provided in the data, that is(14)
For some regions of the parameter space, posterior inference will be determined purely by prior beliefs even if the sample size is infinite, since the data are uninformative. This is a standard property of Bayesian inference in partially identified models, as discussed for example in Kadane (1975), Poirier (1998), and Moon and Schorfheide (2012).
Much of ingenuity and creativity in the SVAR literature has been devoted to provide arguments—or "identification schemes"—about the appropriate choice of p(Q|A, Σ).6 These arguments translate into what can be viewed as Bayesian inference with dogmatic prior beliefs—or distributions with singularities—about the conditional distribution of Q, given the reduced form parameters. For example, the commonly applied recursive identification amounts, from a Bayesian perspective, to assuming with dogmatic certainty that all of the upper diagonal elements of B0 are zero, while we do not have any information on the other values of B0. Equivalently, it assumes with certainty that . Similarly, other commonly used identifications—for example, long-run, medium-run, sign restrictions, etc.—can be expressed in terms of probabilistic a priori statements about the parameters in B0.
Once a B0 matrix is selected, dynamic causal effects of the identified structural shocks on the variables in yt are usually summarized by the structural impulse response functions (IRFs). In a VAR(p), they can be recursively calculated as(15)
, and Aτ are the reduced form autoregressive coefficients of Eq. (1) with Aτ = 0 for τ > p. The (i, j) element of IRFh denotes the response of variable i to shock j at horizon h. Uncertainty about dynamic responses to identified structural shocks is typically reported in the Bayesian literature as point-wise coverage sets around the posterior mean or median IRFs, at each horizon—that is, as the appropriate quantiles of the IRFs posterior distribution. For example, 68% of coverage intervals can be reported as two lines representing the posterior 16th and 84th percentiles of the distribution of the IRFs. Such credible sets usually need to be interpreted as point-wise (i.e., as credible sets for the response of a specific variable) to a specific shock, at a given horizon. However, point-wise bands effectively ignore the existing correlation between responses at different horizons. To account for the time (horizon) dependence, Sims and Zha (1999) suggested to use the first principal components of the covariance matrix of the IRFs.
Sims and Zha (1998) discussed a general framework for Bayesian inference on the structural representation in Eq. (11). Rewrite the SVAR as(17)
where the T × n matrices y and e and the T × k matrix x are defined as(18)
and B = [B1,…, Bp,Bc]. The likelihood can be written as(19)
where |B0| is the determinant of B0 (and the Jacobian of the transformation of e in y). Conditional on B0, the likelihood function is a normal distribution in B. Define β ≡ vec(B) and β0 ≡ vec(B0). A prior for the SVAR coefficients can be conveniently factorized as(20)
where p(β0) is the marginal distribution for β0, and can include singularities generated by, for example, zero restrictions. The (conditional) prior for β can be chosen to be a normal p.d.f.7(21)
The posterior distribution of β is hence of the standard form(22)
where the posterior moments are updated as in the standard VAR with Normal-Inverse Wishart priors (see, e.g., Kadiyala & Karlsson, 1997). The posterior for β0 will depend on the assumed prior.8
Baumeister and Hamilton (2015) applied a streamlined version of this framework to provide analytical characterization of the informative prior distributions for impulse-response functions that are implicit in a commonly used algorithm for sign restrictions. Sign restrictions are a popular identification scheme, pioneered in a Bayesian framework by Canova and De Nicolo (2002) and Uhlig (2005). The scheme selects sets of models whose B0 comply with restrictions on the sign of the responses of variables of interests over a given horizon. Bayesian SVARs with sign restrictions are typically estimated using algorithms such as in Rubio-Ramírez, Waggoner, and Zha (2010), where a uniform (or Haar) prior is assumed for the orthogonal matrix. Operationally, a n × n matrix X of independent values is generated and decomposed using a QR decomposition where Q is the orthogonal factor and R is upper triangular. The orthogonal matrix is used as candidate rotation Q, and the signs of the responses of variables at the horizons of interest are assessed against the desired sign restrictions. Baumeister and Hamilton (2015) showed that this procedure implies informative distributions on the structural objects of interest. In fact, it implies that the impact of a one standard-deviation structural shock is regarded (before seeing the data) as coming from a distribution with more mass around zero when the number of variables n in the VAR is greater than 3 (and with more mass at large values when n = 2). It also implies Cauchy priors for structural parameters such as elasticities. The influence of these priors does not vanish even asymptotically, since the data do not contain information about Q. In fact, as the sample size goes to infinity, the height of the posterior distribution for the impact parameters is proportional to that of the prior distribution for all the points in the parameter space for which the structural coefficients satisfy the set restrictions that orthogonalize the true variance-covariance matrix.
Giacomini and Kitagawa (2015) suggested the use of "ambiguous" prior for the structural rotation matrix in order to account for the uncertainty about the structural parameters in all under-identified SVARs. The methodology consists in formally incorporating in the inference all classes of priors for the structural rotation matrix, which are consistent with the a priori "dogmatic" restrictions. In a similar vein, Baumeister and Hamilton (2017) discussed how to generalize priors on B0 to a less restrictive formulation that incorporates uncertainty about the identifying assumptions themselves, and used this approach to study the importance of shocks to oil supply and demand.
Large Bayesian VARs
The size of the VARs typically used in empirical applications ranges from three to a dozen variables. VARs with larger sets of variables are impossible to estimate with standard techniques due the "curse of dimensionality" induced by the densely parametrized structure of the model.9 However, in many applications there may be concerns about the omission of many potentially relevant economic indicators that may affect both structural analysis and forecasting.10 Additionally, big datasets are increasingly important in economics to study phenomena in a connected and globalized world, where economic developments in one region can propagate and affect others.11
VARs involving tens or even hundreds of variables have become increasingly popular following the work of Bańbura et al. (2010), which showed that standard macroeconomic priors—Minnesota and sums-of-coefficients—with a careful setting of the tightness parameters allowed to effectively incorporate large sets of endogenous variables. Indeed, a stream of papers have found large VARs to forecast well (see, e.g., Bańbura et al., 2010; Carriero, Clark, & Marcellino, 2015a; Carriero, Kapetanios, & Marcellino, 2009; Giannone, Lenza, Momferatou, & Onorante, 2014; Koop, 2013).
Early examples of higher-dimensional VARs are Panel VARs, where small country-specific VARs are interacted to allow for international spillovers (see, e.g., Canova & Ciccarelli, 2004, 2009). These models can be seen as large-scale models that impose more structure on the system of equations. Koop and Korobilis (2015) studied methods for high-dimensional panel VARs. In the study of international spillovers, an alternative to Panel VARs are Global VARs (Pesaran et al., 2004). A Bayesian treatment to G-VARs is in Cuaresma, Feldkircher, and Huber (2016).
A recent development in this literature has been the inclusion of stochastic volatility in Large BVAR models. Carriero et al. (2016a) assumed a factor structure in the stochastic volatility of macroeconomic and financial variables in Large BVARs. In Carriero et al. (2016b), stochastic volatility and asymmetric priors for large n are instead handled using a triangularization method that allows for the simulation of conditional mean coefficients of the VAR by drawing them equation by equation. Chan et al. (2017) propound composite likelihood methods for large BVARs with multivariate stochastic volatility that involve estimating large numbers of parsimonious submodels and then taking a weighted average across them. Koop, Korobilis, and Pettenuzo (2016) discuss large Bayesian VARMA. Koop (2017) reviews the applications of big data in macroeconomics.
Bayesian VARs and Dynamic Factor Models
Research started with Bańbura et al. (2010) has shown that large BVARs are competitive models in leading with large-n problems in empirical macroeconomics, along with factor models (see Forni, Hallan, Lippi, & Reichlin, 2000; Stock & Watson, 2002) and factor-augmented VARs (FAVARs, see Bernanke, Boivin, & Eliasz, 2005). Indeed, Bayesian VARs are strictly connected to factor models as shown by De Mol et al. (2008) and Bańbura et al. (2015).
The link can be better understood in terms of data that have been transformed to achieve stationarity, Δyt, and that have been standardized to have zero mean and unit variance. A VAR in first differences can be written as(23)
Imposing the requirement that the level of each variable yt must follow an independent random walk process is equivalent to requiring its first difference Δyt to follow an independent white noise process. Hence, the prior on the autoregressive coefficients in Eq. (23) can be characterized by the following first and second moments:(24)
The covariance between coefficients at different lags is set to zero. Since the variables have been rescaled to have the same variance, we can set , where .
Denote the eigenvalues of the variance-covariance matrix of the standardized data by ζj, and the associated eigenvectors by υj, for j = 1, . . . , n, i.e.(25)
where if i = j and zero otherwise. We assume an ordering such that . The sample principal components of Δyt are defined as(26)
The principal components transform correlated data, Δyt, into linear combinations which are cross-sectionally uncorrelated and have unit variance, that is . The principal components can be ordered according to their ability to explain the variability in the data, as the total variance explained by each principal component is equal to ζj.
Rewrite the model in Eq. (23) in terms of the ordered principal components, as(27)
The priors that impose a uniform shrinkage on the parameters in Eq. (24) map into a non-uniform shrinkage on the parameters in Eq. (27):(28)
Importantly, the prior variance for the coefficients on the j-th principal component is proportional to its share of explained variance of the data .
If the data are characterized by a factor structure, then, as n and T increase, ζj will go to infinity at a rate n for j = 1, . . . , r where r is the number of common factors. Conversely, will grow at a slower rate, which cannot be faster than . If λ1 is set such that it converges to zero at a rate that is faster than that for the smaller eigenvalues and slower than that for the largest eigenvalues, for example , with 0 < ϱ < 1/2, then λ1 ζj will go to infinity for j = 1,…,r and the prior on the coefficients associated with the first r principal components will become flat (see Bańbura et al., 2015). Conversely, the coefficients related to the principal components associated with the bounded eigenvalues will be shrunk to zero, since λ1 ζj will go to zero for j > r.
De Mol et al. (2008) show that if the data are generated by a factor model and λ1 is set according to the rate described above, the point forecasts obtained by using shrinkage estimators converge to the unfeasible optimal forecasts that would be obtained if the common factors were observed.
Large SVARs, Non-Fundamentalness
One of the open problems in SVARs is the potential "non-fundamentalness" of structural shocks for commonly employed VARs (a review on this issue is in Alessi, Barigozzi, & Capasso, 2011). Non-fundamentalness implies that the true structural shocks (i.e., et in Eq. 11) cannot be retrieved from current and past forecast errors of the VARs of choice (see Hansen & Sargent, 1980; Lippi & Reichlin, 1994). This situation arises when, for example, the econometrician does not have all the information available to economic agents, such as news about future policy actions. This is notoriously the case for fiscal shocks, as explained in Leeper, Walker, and Yang (2013). In this case, economic agents' expectations may not be based only on the current and past yt, implying that the residuals of the reduced-form model (i.e., ut in Eq. 1) are not the agents' expectation/forecast errors. As a consequence, the shocks of interest may not be retrieved from the forecast errors and may be non-fundamental. A possible solution is to allow for noninvertible moving average (MA) components. A different strategy is to view non-fundamentalness as an omitted variables problem. In this respect BVARs (and factor models) can offer a solution to the incorporation of larger information sets. For example, Ellahie and Ricco (2017) discussed the use of large BVARs to study the propagation of government purchases shocks, while controlling for potential non-fundamentalness of shocks in small VARs.12
Forecasting in Data-Rich Environments
A research frontier is the application of Bayesian VARs to forecasting in data-rich environment, where the predictive content of large datasets (typically counting 100 or more variables) is exploited to forecast variables of interest. A recent survey is in Bok et al. (2017).
Bańbura et al. (2010) studied the forecasting performance of large Bayesian VARs. They found that while it increases with model size—provided that the shrinkage is appropriately chosen as a function of n –, most of the gains are in fact achieved by a 20-variable VAR. Evaluation of the forecasting performance of medium and large Bayesian VARs is also provided in Koop (2013). Carriero et al. (2011) evaluated the forecasting accuracy of reduced-rank Bayesian VARs in large datasets. The reduced-rank model adopted has a factor model underlying structure, with factors that evolve following a VAR. Korobilis (2013) extended the framework to allow for time-varying parameters. Giannone, Lenza, and Primiceri (2017) argued in favor of dense (as opposed to sparse) representations of predictive models for economic forecasting and use a "spike-and-slab" prior that allows for both variable selection and shrinkage.
BVARs are also a valuable tool for real-time forecasting and nowcasting with mixed-frequency datasets. In fact, they can be cast in state-space form, and filtering techniques can be easily used to handle missing observations, data in real time, and data sampled at different frequencies. Ghysels (2016) introduced a class of mixed-frequency VAR models that incorporated data sampled at different frequencies and discussed Bayesian approaches to the estimation of these models. Recent examples of these applications include Korobilis (2013); Schorfheide and Song (2015); Carriero et al. (2015b); Brave, Butters, and Justiniano (2016); Clark (2011); Giannone et al. (2014); and McCracken, Owyang, and Sehkposyan (2015).
Koop, Korobilis, and Pettenuzo (2016) proposed the use of Bayesian compressed VARs for high-dimensional forecasting problems and found that these tend to outperform both factor models and large VAR with prior shrinkage. More recently, Kastner and Huber (2017) developed BVARs that can handle vast dimensional information sets and also allow for changes in the volatility of the error variance. This is done by assuming that the reduced-form residuals have a factor stochastic volatility structure (which allows for conditional equation-by-equation estimation) and by applying a Dirichlet-Laplace prior (Bhattacharya, Pati, Pillai, & Dunson, 2015) to the VAR coefficients that heavily shrinks the coefficients toward zero while still allowing for some non-zero parameters. Kastner and Huber (2017) provided MCMC-based algorithms to sample from the posterior distributions and showed that their proposed model typically outperforms simpler nested alternatives in forecasting output, inflation, and the interest rate.
We thank the editors of the Oxford Research Encyclopedia of Economics and Finance and two anonymous referees for useful comments and suggestions. We are also grateful to Fabio Canova, Andrea Carriero, Matteo Ciccarelli, Domenico Giannone, Marek Jarocihski, Marco del Negro, Massimiliano Marcellino, Giorgio Primiceri, Lucrezia Reichlin, and Frank Shorfheide for helpful comments and discussions. The views expressed in this paper are those of the authors and do not necessarily reflect those of the Bank of England or any of its committees.
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Koop, G. (2013, March). Forecasting with medium and large Bayesian VARS. Journal of Applied Econometrics, 28(2), 177–203.Find this resource:
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Koop, G., & Korobilis, D. (2012). Forecasting inflation using dynamic model averaging. International Economic Review, 53(3), 867–886.Find this resource:
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(1.) An extreme version of lack of sample information arises in this context. In fact, structural VARs can be parametrized in terms of reduced form VARs that capture the joint dynamics of economic variables, and an "impact matrix" describing the casual connection between stochastic disturbances and economic variables. This matrix is not uniquely identified by sample information, and hence the investigator has to elicit prior beliefs on it (see Sims & Zha, 1998; Baumeister & Hamilton, 2015).
(3.) Since the work of Sims et al. (1990), it is common practice to keep variables in (log-)levels. However, whether to employ in VARs variables in growth rates, log-levels, or levels remains an empirically important question. In the context of forecasting, for example, Carriero et al. (2015a) recommend the use of differenced data. Carriero et al. (2015a) also found that overall the differences between the iterated and direct forecasts are small, but there are large gains from the direct forecast for some of the variables. This is presumably because the direct forecast is more robust to misspecification.
(4.) Other relevant contributions on density forecast combination are Waggoner and Zha (2012); Geweke and Amisano (2011); Hall and Mitchell (2007); Billio et al. (2013); Amisano and Geweke (2017); Raftery et al. (2010). Applications are in Hwang (2017); Koop and Korobilis (2012), and Aastveit et al. (2017) in the context of real-time forecasting.
(5.) It is assumed that the information in the history of yt is sufficient to recover the structural shocks et, that is, it is possible to write the structural shocks as a linear combination of the reduced form innovations ut. In this case, it is said that the shocks are fundamental for yt. Departures from this case are discussed in section "Large Bayesian VARs." Relevant references are provided therein.
(6.) A survey of the identification schemes proposed in the literature goes beyond the scope of this article. A recent textbook treatment on the subject is in Kilian and Lütkepohl (2017).
(7.) As it is usually done in the literature, Sims and Zha (1998) suggested preserving the Kronecker structure of the likelihood to avoid the inversion of nk×nk matrices and gain computational speed.
(8.) Canova and Pérez Forero (2015) provided a general procedure to estimate structural VARs also in the case of overidentified systems where identification restrictions are of linear or of nonlinear form.
(9.) The number of parameters to be estimated in an unrestricted VAR increases in the square of n, the number of variables in yt. Even when mechanically feasible—that is, when the number of available data points allows to produce point estimates for the parameters of interest—the tiny number of available degrees of freedom implies that parameters are estimated with substantial degrees of uncertainty and typically yield imprecise out-of-sample forecasts.
(10.) A standard example of this has been the debate about the so-called price puzzle—positive reaction of prices in response to a monetary tightening—that is often found in small scale VARs (see, e.g., Christiano, Eichenbaum, & Evans, 1999). The literature has often connected such a puzzling result as an artifact resulting from the omission of forward-looking variables such as the commodity price index. In fact, one of the first instances of VARs incorporating more than a few variables was the 19-variable BVAR in Leeper, Sims, and Zha (1996) to study the effects of monetary policy shocks.
(11.) Large datasets of macroeconomic and financial variables are increasingly common. For example, in the United States, the Federal Reserve Bank of St. Louis maintains the FRED-MD monthly database for well over 100 macroeconomic variables from 1960 to the present (see McCracken & Ng, 2015), and several other countries and economic areas have similarly sized datasets.


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What Are Native Apps ?
Native applications are apps developed with a specific platform in mind. For example, you could have a native mobile application that is intended to run exclusively in iOS, the operating system used by Apple mobile devices. Native apps have a tendency to take full advantage of the features of a given operating system, but it can take more time and effort to keep them up to date. For example, an iOS app must continuously evolve to make use of new hardware features on iPhone handsets. Native mobile app development is great for products targeted to specific types of mobile users.

subset: commoncrawl


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During the many discussions with several frame stiffness testing centers and providers my understanding that frame stiffness tests are largely done in a somewhat inexact manner, mostly lacking any statistical validity, was reconfirmed. A quote from one engineering lab in particular sums up the situation nicely:
"And, to be perfectly honest, the bicycle industry has not been particularly demanding or even interested in a higher level of scientific rigour…"
So with that in mind I decided on our future steps which will produce highly credible, repeatable results. For the present however we decided to fall back to the magic of statistics and cohort analysis. We have stiffness data from one of our manufacturing partners and they happen to make several brands of frames that were extensively tested by the Tour Magazin over the years. Helpfully, Tour data shows useful consistency over time. We also know our own frame stiffness data since each of our carbon frames is tested for stiffness as part of our basic QC procedure. Thus with the other brand's frames serving as a cipher key to correlate Tour test data with our own test data to within 5% accuracy (confidence interval actually) we can now present a comprehensive list of published Tour data for 19 competitors, and for three of our frames: Velocite Magnus, Velocite Geos, and Velocite Selene.
Normalized data according to Tour Magazin method
Torsional Stiffness Headtube N/deg
Torsional Stiffness Bottom Bracket N/mm
BMC Racemachine RM01 78 56
Velocite Geos 82 122
Neil Pryde Diablo 86 52
Scott Addict RC 86 54
Trek Madone 6 SSL 88 56
Scott Foil 40 90 56
Lapierre Sensium 400 CP 91 57
Koga Kimera 91 62
Specialized Venge 92 53
KTM Strada 5000 94 56
Canyon Ultimate CF 9.0 Pro 94 60
Cervelo R3 SL 94 65
Koga Kimera UD Team 98 60
Giant TCR Advanced SL (ISP) 98 67
Focus Izalco Pro 3.0 99 56
Cervelo R5 CA 101 70
Felt F4 102 59
Merida Reacto 909 104 61
Fuji Altamira 2.3 104 63
Storck Fascenario 0.7 104 69
Cannondale Supersix 3 105 65
Cannondale Super Six EVO 107 53
Velocite Selene (alloy) 109 125
Specialized Tarmac SL3 112 72
Velocite Magnus 131 146
The data in the table was sourced from Tour Quarterly online publication, from Tour Magazin print edition for November 2011 and April 2013. Only the data for German market specific online brands was excluded to increase relevance and chart legibility. Some current frame models are missing from the chart, but that is due to lack of Tour test data, not deliberate omission.
Chart 1: Frame torsional stiffness as measured at the head tube and ranked from highest to lowest. Click to enlarge.
Chart 1 shows torsional stiffness as measured at the head tube, ranked from lowest (BMC Racemachine RM01) to highest (Velocite Magnus). This is the traditional way of ranking stiffness when other manufacturers talk about how stiff their frames are. Torsional stiffness at the head tube influences how well the bike will steer, all other things being equal. We can see that the best regarded brands (Specialized, Cannondale, Cervelo) all rank pretty high which is good to see as it shows that there is a degree of competition in the market based on product performance. It also shows that at the high end of the market, riders may get what they are paying for.
Coming to Velocite products, Velocite Magnus exceeds the torsional stiffness of the second stiffest frame by 17% while the Velocite Selene ranks third and Velocite Geos comes second from the end in a field of what are regarded as top end frames.
However as the second chart will show, this is not the entire story. What is not normally emphasised by the bike brands is stiffness at the bottom bracket, the pedaling stiffness. This measure describes how efficient the frame is in moving you forward. In simplified terms, the stiffer the frame is at the bottom bracket, the quicker it responds to your pedaling inputs and the more energy efficient it is. While the frame itself is just about perfectly elastic meaning that when looked at in isolation the frame flexing does not actually lose energy, the energy loss occurs as soon as you place a rider on the bike due to the rider being just about the perfect hysteresis material (damper, energy sink). Thus the stiffer the frame is at the bottom bracket, the quicker it will respond and the more efficient it will be in converting your energy into forward motion instead of wasting it.
Chart 2: Frame stiffness expressed as a sum of both torsional stiffness at the head tube and torsional stiffness at the bottom bracket (pedaling stiffness). Click to enlarge.
This second chart (Chart 2) therefore shows a somewhat more complete picture. All the Velocite road frames rank significantly higher than any other high end frame. In fact the Velocite Magnus is cumulatively twice as stiff as the least stiff of the high end frames studied and 51% stiffer than the next stiffest non Velocite frame. This stiffness advantage is not marginal, barely perceptible or within the test protocol margin of error, it is significant and large. You can feel the difference immediately when you ride any of our bikes. What are the drawbacks of this extraordinary torsional stiffness? There are none, except for the higher frame manufacturing costs which we absorb. Despite their stiffness none of our frames are uncomfortable, none experience any rear wheel skipping and none of them are fragile. This exceptional performance is essentially free to the rider.
Why don't other brands pursue pedalling stiffness? They in fact do and they spend large portions of their technology descriptions, press releases and training materials describing how they focus on gaining the maximum pedalling stiffness. Some even invent new super wide bottom bracket standards in the pursuit of pedalling efficiency. Other brands on the other hand make claims that "beyond certain point, there is no difference" – this is entirely incorrect due to simple physics. Frames operate in a perfectly elastic domain, well below their yield or non-linear behaviour meaning that every rider regardless of their strength is flexing the frame in the same ratio of force vs. deflection or N/mm.
We also did not achieve this performance leadership by making our frames heavy. For instance the Magnus size L frame (57cm) is just 1180g, painted and with all fittings. This compares very favourably with actual (not claimed) weights of the other frames studied.
To account for frame weight, for some time now many brands have been using an abstract measure of "stiffness to weight ratio" (STW) to describe the performance of their frames. The STW ratio is derived by dividing torsional stiffness at the head tube by the frame's weight in kg. I call this an abstract measure as STW ratio (also known as specific modulus) is only useful to characterise mechanical properties of materials being used. STW ratio is entirely without merit when it is used to imply performance benefits in structures such as frames or wheels. Riders do not experience stiffness to weight ratio, they experience stiffness and weight separately. This is in contrast to the power to weight ratio which is directly related to how far and how fast you will go.
Nevertheless, high STW ratios have become the new marketing battleground with each new exotic and inevitably ultralight frame vying for the STW ratio crown. Thus here is a table listing the stiffness to weight ratios of tested frames, ranked in order of increasing STW ratio as measured at the head tube.
Normalized data according to Tour Magazin method
STW Ratio Headtube N/deg/kg
STW Ratio Bottom Bracket N/mm/kg
Velocite Selene (alloy) 75 86
BMC Racemachine RM01 76 55
Velocite Geos 77 115
Lapierre Sensium 400 CP 77 48
Koga Kimera UD Team 77 47
Focus Izalco Pro 3.0 77 44
Neil Pryde Diablo 80 48
Fuji Altamira 2.3 81 49
KTM Strada 5000 85 51
Felt F4 85 49
Scott Foil 40 86 53
Cannondale Supersix 3 86 53
Canyon Ultimate CF 9.0 Pro 88 56
Merida Reacto 909 98 57
Scott Addict RC 100 63
Giant TCR Advanced SL (ISP) 100 69
Trek Madone 6 SSL 105 67
Velocite Magnus 111 124
Storck Fascenario 0.7 115 76
Specialized Tarmac SL3 127 82
Cervelo R5 CA 137 95
Cannondale Super Six Evo 151 75
Looking at the table above you can see the foundations of the performance claims made by the market leaders. Our first contender, the Velocite Magnus falls to the 5th spot in the STW ratio at the head tube table and the otherwise excellent alloy Selene now owns the last spot in this ranking due to its higher weight.
Let's then have a look at what happens when the table is sorted according to the STW ratio as measured at the bottom bracket. This STW ranking is occasionally used by some manufacturers to establish dominance over their intended competitor.
Normalized data according to Tour Magazin method
STW Ratio Headtube N/deg/kg
STW Ratio Bottom Bracket N/mm/kg
Focus Izalco Pro 3.0 77 44
Koga Kimera UD Team 77 47
Lapierre Sensium 400 CP 77 48
Neil Pryde Diablo 80 48
Fuji Altamira 2.3 81 49
Felt F4 85 49
KTM Strada 5000 85 51
Scott Foil 40 86 53
Cannondale Supersix 3 86 53
BMC Racemachine RM01 76 55
Canyon Ultimate CF 9.0 Pro 88 56
Merida Reacto 909 98 57
Scott Addict RC 100 63
Trek Madone 6 SSL 105 67
Giant TCR Advanced SL (ISP) 100 69
Cannondale Super Six Evo 151 75
Storck Fascenario 0.7 115 76
Specialized Tarmac SL3 127 82
Velocite Selene (alloy) 75 86
Cervelo R5 CA 137 95
Velocite Geos 77 115
Velocite Magnus 111 124
Frame stiffness to weight ratio (STW) ranked according to the STW at the bottom bracket. Click to enlarge.
Things look a lot different now when the pedalling efficiency and responsiveness are ranked according to the frame weight. The Velocite Magnus now dominates this ranking convincingly, even against the very expensive, limited run and still very exotic ultra light frames. In fact the Magnus STW ratio is 30% higher than the next non Velocite competitor while the Geos STW ratio is 21% higher. Selene now also returns to the top part of the table with a solid 4th spot.
So what does the high STW ratio tell us when looking at modern bicycle frames? Nothing. Sorry. Please always look for absolute numbers, not a ratio of stiffness per unit weight unless you are choosing which material to use in your next project.
In conclusion, while attempting to maintain some objectivity it is clear that the Velocite Magnus, Velocite Geos and Velocite Selene are very special frames. They are either the stiffest outright or even dominate the synthetic STW ratio measure when it comes to their stiffness at the bottom bracket (pedalling efficiency). They also achieve this performance dominance not through excessive weight, but by focus on real engineering, material choice and advanced manufacturing methods.
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Bookmark this page for the latest Transfer News and Rumours news every day. All times shown are GMT. E-Soccer.com brings you the latest headlines from the best Transfer News and Rumours national and international news sources.
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The NetBeans platform is an application runtime - a "generic large desktop application." Most desktop applications have common requirements - menus, document management, settings and so forth. Nobody enjoys writing menu code or setting storage code. With the NetBeans Platform, you don't have to. Just write modules to implement what you need, bundle them up with the NetBeans Platform, and you have a beautiful, branded, cross-platform application. And if you need custom functionality or components, the Platform is built to be flexible.
With the NetBeans platform, developers get to concentrate on the important parts of an application - the business logic that makes that application unique. The result is a huge savings in time and effort. Some of the features of the platform are:
- User interface management - Windows, menus, toolbars and other presentation components are provided by the Platform. Developers write to a set of abstractions such actions and components, saving time, and producing cleaner, more bug-free code. Custom components and behaviors can be written, but for most cases this is not needed.
- Data and presentation management - The NetBeans Platform contains a rich toolset for presentating data to the user and manipulating that data.
- The Editor - Available as an extension to the Platform, applications built on NetBeans can use the NetBeans Editor, a powerful and extensible toolset for building custom editors.
- Setting management - The NetBeans Filesystems infrastructure abstracts file-based data. Files may exist locally or remotely, on FTP or CVS servers or in a database; access to them is transparent to module code that works with files. The Platform can be extended to support new forms of storage. Applications built on NetBeans are internet-ready!
- The Wizard framework - a toolset for easily building extensible, user-friendly Wizards to guide users through more complex tasks.
- Configuration management - Rather than tediously write code to access remote data and manage and save user-configurable settings, etc., all of this is handled by the Platform. Applications consist of the platform and the logic code important to that application.
- Storage management - An abstraction of file-based data access. "Files" in the NetBeans paradigm may be local files, or exist remotely, for example, on an FTP server, CVS repository or in a database. Where this data is stored is completely transparent to other modules that work with this data.
- Cross-platform - since the Platform is written entirely in the Java language, applications based on it, by their very nature, will run on any operating system with a Java 2 compatible (1.3 or greater) JVM.
Licence: Sun Public License


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"My family is impressed by his attentiveness, along with the helpfulness of his staff. We're happy to refer Ashforth Chiropractic Family Wellness Center to all of our family and friends!"
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NEW YORK — The U.S. Virgin Islands are issuing hundreds of dollars in gift cards to people who book a trip to the islands.
As part of the 100 year anniversary of Transfer Day, travelers can get $300 of spending credits for historical and cultural tours of the islands as well as activities. Offers are available in St. Croix, St. John and St. Thomas.
Transfer Day is a holiday celebrated on March 31 when Denmark transferred the Virgin Islands to the United States for $25 million in gold. According to the official USVI website, the islands saw a surge in tourism when the United States imposed an embargo on Cuba in 1959.
Travelers can book the trip through Oct. 1, 2017. The travel window is from Jan. 2, 2017 to Dec. 31, 2017.
If you visit in March, you'll get a commemorative "Centennial" souvenir.
There is a minimum stay of three nights at participating hotels.
For more information go to the VisitUSVI website.


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Vancouver, British Columbia (PRWEB) August seventeen, 2013 Functioning from residence is something that is getting to be much more and much more frequent throughout the planet and according to the Australian Bureau of Data, nearly 2.4 million Australians operate from house at the very least one day a week.* &#thirteen To appear into the situation […]Read the rest of this entry »
The College of Web Marketing Founder James Martell Interviews Sarah Bundy on Managing a Organization from Residence
Start-Up Newzful Can make Content Advertising and marketing Effortless
Los Angeles, CA (PRWEB) August thirteen, 2013 Newzful, a new reality-finding subscription service, offers stats, facts, knowledge and information for organizations engaged in material marketing and advertising. Each and every 7 days, Newzful sends subscribers an email stocked with information from 1 of fourteen industries and topics. &#thirteen Newzful addresses industries and topics which includes:&#thirteen […]Read the rest of this entry »
Marketing Campaign Launched by Zhou & Chini: Quit Wage Garnishment & File Unexpected emergency Individual bankruptcy in Los Angeles
Los Angeles, California (PRWEB) August 07, 2013 These are difficult financial times brought on by a struggling economic and slow housing recovery. Many Los Angeles families and businesses have fallen into the downward spiraling pit of unpaid debt. As debts accumulate, it can be even harder to make payments. Then, when lenders and bill collectors […]Read the rest of this entry »
Enhancing Economic system and Conference Attendance is Key Contributor to Surge in Marketing Advertising and Income of Neck Lanyards According to Sector Chief PinMart, Inc.
Chicago, Illinois (PRWEB) August 06, 2013 Every single summertime, hundreds of conventions are held all over the United States. These functions cater to various vocations, professions, and hobbies, and serve to supply a conference spot for professionals and fanatics to share data, make connections, and offer related and chopping-edge products. Normally, registration for a conference […]Read the rest of this entry »
Orlando Advertising and marketing Organization TAG Company Strategies Awarded Unique Gold Offer Partner Position by Golds Health club Franchise Affiliation
Orlando, Florida (PRWEB) July 24, 2013 TAG Business Approaches, a leader in net, regional search and social advertising and marketing for franchise proprietors and little firms, is delighted to announce it has been awarded the coveted Gold Source Spouse Status with the Golds Gymnasium Franchise Affiliation right after file-breaking advertising accomplishment in numerous Florida Golds […]Read the rest of this entry »
Seo Manual for Company Homeowners: 5 Ideas for Diy Search engine marketing
(PRWEB) July 29, 2013 In Seo Information for Company Proprietors: 5 Ideas for Do it yourself Search engine optimisation Gollan counsels that there is a notion Search engine marketing must be remaining to qualified authorities in the discipline. But he insists there are loads of Do it yourself ideas to put into action without having […]Read the rest of this entry »
Catering Marketing 360 Finds Huge Need for Successful Catering Marketing and advertising as Food Need Proceeds to Increase with Developing Population
Loveland, Colorado (PRWEB) July 19, 2013 Catering Marketing 360, the leader in targeted net advertising and marketing, finds that demand from customers for efficient catering marketing and advertising is at an all-time large. According to Datamonitor, The planet foodservice industry is predicted to achieve virtually $ 992 billion in 2014. Catering providers make up a […]Read the rest of this entry »
The Pedowitz Group Bridges Gap In between Business Intelligence and Income Progress with New Revenue Advertising and marketing Analytics Follow for Business Clients
Atlanta, GA (PRWEB) July 25, 2013 The Pedowitz Group, the global chief in Income Marketing and advertising, announces a new Income Marketing and advertising Analytics support giving that bridges the gap among organization intelligence and income development. To that finish, The Pedowitz Group has partnered with two Company Intelligence technologies market place leaders, Birst and […]Read the rest of this entry »


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- This Semi-Automatic Filling Machine for Liquid is designed to handle water-thin to very viscous fluids.
- Machine construction covered by SUS#304
- Filling pump driven by variable speed AC motor with inverter.
- Pneumatic ball Valve, special design for easy cleaning.
- Foot pad control for filling action is available.
- Photocell sensors (Germany , SICK brand).
- Agitator with a variable speed AC motor for mixing liquid.
- Liquid Feeding Pump with a stand.


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Tuesday, 5 February 2013
Must read those books...
I have a Kindle that has 645 books on it...most of which I have downloaded for free - some have been excellent, some not so much and no I haven't read all 645 yet...
But I do love 'real' books especially and I do have a LOT. Probably split equally between witchcraft/pagan non fiction books and fantasy fiction with a few classics thrown in for good measure (Sherlock Holmes, Alice in Wonderland and all the Roald Dahl to name a few). I always buy a real book if one of my favourite authors has a new book out, I have a paperback copy and a hardback copy of every single Terry Pratchett book for instance :-)
With the fiction books I tend to read them straight away (especially if it is a Pratchett one which usually gets read cover to cover in one sitting) but I find with the non fiction books I buy them because they look interesting or because I am researching a particular subject, it arrives, I have a flick through it and maybe read the odd page or two then it often gets put on the shelf and left there...looking pretty.
I am also a bit of a book Nazi...folding over corners of pages is a serious crime in our house ;-) Books are meant to be cared for, cherished and looked after.
One of the challenges I have set myself for this year is to actually read, cover to cover, all of the non fiction Craft books that I have, I am sure there must be some amazing information inside those covers that I am missing out on.
So wish me luck...I've made a list...and I am on a mission...
3 comments:
I am so the same with books. No way do you fold pages. :) Been reading since I was 3...now 41. Also do the non-fiction thing as well. But have a few dozen books in general. Nothing beats a real book that's for sure. The smell, feel of paper as you go into another world. :)ReplyDelete
LOL, I love the "real" books too, but I am the exact opposite. You can tell the non-fiction or metaphysical books that I enjoyed because there are notes written in the margins and underlines and exclamation points and asterisks all over the place. You can tell the fiction books that I enjoyed because there are many page corners folded over, from many readings of the book. I have a friend whose books are pristine, and she would never lend me even one; do you blame her? I don;t.ReplyDelete
Do I have a Kindle? Yup, also filled with many free books that I haven't had the time to read yet. But there is something about holding a much-loved book, cracking spine and bent or marked-up pages and all, in my hands. Like putting on a pair of worn yet comfortable slippers.
Now, all we need is a nice cup of tea, a comfy chair, and a cozy fire!
You are right, nothing beats a real book! I do have quite a few text books that I have worked from but I just can't bring myself to write on them...so they are full of lots of sticky post it notes with scribblings on LOLReplyDelete


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Year of publication
- Pion Suppression in Nuclear Collisions (1997)
- The pion multiplicity per participating nucleon in central nucleus-nucleus collisions at the energies 2-15 A GeV is significantly smaller than in nucleon-nucleon interactions at the same collision energy. This effect of pion suppression is argued to appear due to the evolution of the system produced at the early stage of heavy-ion collisions towards a local thermodynamic equilibrium and further isentropic expansion.
- On the early stage of nucleus-nucleus collisions (1998)
- A statistical model of the early stage of central nucleus--nucleus (A+A) collisions is developed. We suggest a description of the confined state with several free parameters fitted to a compilation of A+A data at the AGS. For the deconfined state a simple Bag model equation of state is assumed. The model leads to the conclusion that a Quark Gluon Plasma is created in central nucleus--nucleus collisions at the SPS. This result is in quantitative agreement with existing SPS data on pion and strangeness production and gives a natural explanation for their scaling behaviour. The localization and the properties of the transition region are discussed. It is shown that the deconfinement transition can be detected by observation of the characteristic energy dependence of pion and strangeness multiplicities, and by an increase of the event--by--event fluctuations. An attempt to understand the data on J/psi production in Pb+Pb collisions at the SPS within the same approach is presented.
- Evidence for statistical production of J/psi mesons in nuclear collisions at the CERN SPS (1999)
- The hypothesis of statistical production of J/psi mesons at hadronization is formulated and checked against experimental data. It explains in the natural way the observed scaling behavior of the J/psi to pion ratio at the CERN SPS energies. Using the multiplicities of J/psi and eta mesons the hadronization temperature T_H = 175 MeV is found, which agrees with the previous estimates of the temperature parameter based on the analysis of the hadron yield systematics.
- Comment on 'Comparison of strangeness production between A + A and p + p reactions from 2 to 160 A GeV', by J. C. Dunlop and C. A. Ogilvie (2000)
- A recent paper on energy dependence of strangeness production in A+A and p+p interactions written by Dunlop and Ogilvie (Phys. ReV. C61 031901(R) (2000) indicates that there is a significant misunderstanding about the concept of strangeness enhancement and its role as a signal of Quark Gluon Plasma creation. In this comment we will try to clarify some essential points. 25.75.Dw, 13.85.Ni, 21.65.+f
- Baryon number conservation and statistical production of antibaryons (2000)
- The statistical production of antibaryons is considered within the canonical ensemble formulation. We demonstrate that the antibaryon suppression in small systems due to the exact baryon number conservation is rather different in the baryon-free (B=0) and baryon-rich (B>1) systems. At constant values of temperature and baryon density in the baryon-rich systems the density of the produced antibaryons is only weakly dependent on the size of the system. For realistic hadronization conditions this dependence appears to be close to B/(B+1) which is in agreement with the preliminary data of the NA49 Collaboration for the antiproton/pion ratio in nucleus-nucleus collisions at the CERN SPS energies. However, a consistent picture of antibaryon production within the statistical hadronization model has not yet been achieved. This is because the condition of constant hadronization temperature in the baryon-free systems leads to a contradiction with the data on the antiproton/pion ratio in e+e- interactions.
- Charm estimate from the dilepton spectra in nuclear collisions (2001)
- A validity of a recent estimate of an upper limit of charm production in central Pb+Pb collisions at 158 AGeV is critically discussed. Within a simple model we study properties of the background subtraction procedure used for an extraction of the charm signal from the analysis of dilepton spectra. We demonstrate that a production asymmetry between positively and negatively charged background muons and a large multiplicity of signal pairs leads to biased results. Therefore the applicability of this procedure for the analysis of nucleus-nucleus data should be reconsidered before final conclusions on the upper limit estimate of charm production could be drawn.
- Transverse momentum spectra of J/psi and psi prime mesons from quark gluon plasma hadronization in nuclear collisions (2001)
- Recent results on transverse mass spectra of J/psi and psi prime mesons in central Pb+Pb collisions at 158 AGeV are considered. It is shown that those results support a hypothesis of statistical production of charmonia at hadronization and suggest the early thermal freeze-out of J/psi and psi prime mesons. Based on this approach the collective transverse velocity of hadronizing quark gluon plasma is estimated to be <v^H_T> \approx 0.2. Predictions for transverse mass spectra of hidden and open charm mesons at SPS and RHIC are discussed.
- Power law in hadron production (2001)
- In high energy p+p(bar) interactions the mean multiplicity and transverse mass spectra of neutral mesons from eta to Upsilon (m = 0.5 - 10 GeV/c^2) and the transverse mass spectra of pions (m_T > 1 GeV/c^2) reveal a remarkable behaviour: they follow, over more than 10 orders of magnitude, the power-law function:The parameters C and P are energy dependent, but similar for all mesons produced at the same collision energy. This scaling resembles that expected in the statistical description of hadron production: the parameter P plays the role of a temperature and the normalisation constant C is analogous to the system volume. The fundamental difference is, however, in the form of the distribution function. In order to reproduce the experimental results and preserve the basic structure of the statistical approach the Boltzmann factor e^(-E/T) appearing in standard statistical mechanics has to be substituted by a power-law factor (E/Lambda)^(-P).
- Omega, J/psi and psi' production in nuclear collisions and quark gluon plasma hadronization (2002)
- The transverse mass spectra of Omega, J/psi and psi' in Pb+Pb collisions at 158 AGeV are studied within a hydrodynamical model of the quark gluon plasma expansion and hadronization. The model reproduces the existing data with the common hadronization parameters: temperature T=T_H = 170 MeV and average collective transverse velocity v_T = 0.2.
- Omega, J/psi and psi' transverse mass spectra at RHIC (2002)
- The transverse mass spectra of J/psi and psi' mesons and Omega hyperons produced in central Au+Au collisions at RHIC energies are discussed within a statistical model used successfully for the interpretation of the SPS results. The comparison of the presented model with the future RHIC data should serve as a further crucial test of the hypothesis of statistical production of charmonia at hadronization. Finally, in case of validity, the approach should allow to estimate the mean transverse flow velocity at the quark gluon plasma hadronization.


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AttaGirl appreciates the the support of local make-up artists, fashion experts, motivational speakers, and generous hearts to provide labor, supplies and funding for AttaGirl's empowerment workshops and other community services.
If you would like to donate time, unopened cosmetic and personal products, food, funds or anything else you think the ladies we serve might enjoy, please contact us!
We would love to hear from you!


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NADA Miami
Thursday, December 7, 4pm
An acoustic performance from Mac McCaughan and Jim Wilbur of Superchunk, in conjunction with the Merge Records benefit auction for The Southern Poverty Law Center. This will be their first performance in over 20 years.
Presented with Merge Records; located on the patio.
Audio from NADA Miami Special Projects & Programs will be streaming live and archived on know-wave.com.
NADA Miami
December 7–10, 2017
Ice Palace Studios


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MOMS CLUB of SIMI VALLEY, CA - EAST
MOMS Offering Moms Support
MOMS CLUB of SIMI VALLEY, CA - EAST
MOMS Offering Moms Support
The MOMS Club (Moms Offering Moms Support) is an international non-profit organization aimed at providing a supportive, nurturing environment for today's stay at home mom. We meet during the day, when at-home mothers need support most. We believe being a mother shouldn't isolate you so we've created an environment where both you and your child can have fun. At-home mothers of any age children are welcome. You don't have to leave the group when your children enter school! We know you still need support as your children grow.
Our activities include in home play dates, park days, seasonal parties and service projects. We also go on field trips: including visiting the farm, the zoo and museums. Member meetings are held once a month. This is where the members can share ideas or concerns, as well as discuss topics of interest and on occasion listen to a guest speaker. Don't forget Mom's night out; it's a great way to really get to know the other moms.
MOMS Club is a wonderful place for keeping little ones busy during the day. But more than that, it's a place for moms to connect with each other. We are all in this together. It's amazing what a difference having a supportive network of woman can do. Our hope is to create a feeling of sisterhood among all mothers and generate friendships that will last a life time.
If you are interested in joining us, and live in the 93063 area code, please contact us at [email protected] or find us on Meetup.com – MOMS Club Simi Valley – East
If you are not in our area, visit www.momsclub.org to find your local chapter.
"One of the most beautiful qualities of true friendship is to understand and to be understood."


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Why Buy Conference Gifts?
The Darwinian principle- Survival of the fittest aptly applies to the business environment today. The companies therefore, need to be remain active round-the-clock in order to remain in the competition. A company needs to execute all its plans to perfection to fight it out in the market. That's why business organisations are constantly looking out for unique and fresh marketing strategies to stay ahead in the market and to connect itself with its audience.
These promotional strategies are, at times, more than a gamble. But one strategy which can surely make your business a winner is promotion through conference gifts.
Printed conference gifts help you expand your foothold in the market by building distinctive identity for your brand with increased coverage. Promotional conference gifts are great to retain your existing set of clients and at the same time it also allows you to add newer ones. Promotional conference gifts can help to increase the business performance many times.
In short, what can't be achieved by advertising can be accomplished with conference gifts.
We believe that these multiple benefits of conference gifts can help any organisation in the market. For your business to do well and improve its performance in the market, it is necessary that you are steady with your planning. Unlike other types of advertising, which are alleged to be ways of misleading the client, conference gifts are perceived in a different way. These are brilliant ways to show your gratitude for the constant support to your clientele.
Clients love when a business organisation pampers them with freebies.
They expect similar bargains in the future and therefore stick with the business organisation. The other advantage of conference gifts is that it reminds your clients about your brands and services whenever they come across or use the gift item. This is a very important advantage as it keeps your company name alive in the client's mind which may make him buy your products in the near future.
To make greater impact with your conference gifts, you can customise them with vibrant hues and images. With advanced printing techniques, our printing experts will elegantly design the promotional gifts so that it looks exclusive and fashionable. There are plentiful choices available in the market when it comes to choosing products such as conference gifts. You can buy all these items from our online gifts shop at high discount prices. All the promotional products available in our shop are of superior quality.
By: Gareth Parking


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Price: $12.00
Quantity: 1 available
Book Condition: Very Good in Good+ dust jacket
Text is clean. Red cloth with brownish spine lettering. Small dampstain to top edge of spine. Dust jacket shows wear, square of damage to front panel where color lifted off with price sticker, spine of DJ sunfaded and chipped. Previous owner name on front free endpaper. ; Young Moderns Bookshelf; 250 pages
Title: Haunted Airways
Categories: Young Moderns Bookshelf,
Publisher: New York, The Sun Dial Press: 1937
Binding: Hardcover
Book Condition: Very Good in Good+ dust jacket
Item: 1.00 Item
Seller ID: 20894


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Having What It Take To Be a Fucking Petite Model
video info:
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I do not like food that explodes in my mouth.
I have a very simple rule when it comes to food.
I do not like, and will not eat, food that explodes in my mouth.
What, you ask, is food that explodes in your mouth?
Any food that when chewed dramatically changes shape or form with the first chomp or squishing between teeth is a "food that explodes in your mouth." Yes, I know that all food changes form and shape when getting chewed, but this change should naturally happen over the course of a few chews, not in the first explosive chomp.
I am not sure if it is the texture or the change or the "hey, here's another texture or taste you did not expect" surprise that causes this issue for me, but whatever it is I DO NOT LIKE IT.
The key offenders in this category are peas and those little tomatoes. Ever since I ate my first pea in a $.29 pot pie in the third grade, I have had an issue with exploding food.
Just the mention of both of these in the same sentence gives me a nervous chill into my scalp and a bit of throw up in my mouth nausea. And it just dawned on me, but if I were to ever somehow get both a pea and tomato in the same bite, I am pretty sure I would end up hospitalized. Or explode myself.
So if you ever see me anywhere near a pea or a tomato, please in the interest of public, take them and smash them into a million tiny pieces. Then you can shove them in my mouth.
Posted on February 12, 2012, in Uncategorized. Bookmark the permalink. Leave a comment.


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Dr Gomathi Sitharthan, a senior lecturer in Psychology from the Faculty of Health Sciences and Honorary Professor in Psychiatry, Raj Sitharthan from the Sydney Medical School, are inviting anyone who has seen a psychologist in the past two years to participate in an online survey.
"There is good evidence to that access to mental health care has improved," Dr Sitharthan said.
"While more people have access to care, we would like to know from the consumer's point of view what difference the treatment made.
"This University of Sydney study is the first to look at what kind of expectations people had before they saw a psychologist and perhaps most importantly, if they were satisfied with the treatment they received," she said.
The survey – which takes 30 to 40 minutes to complete – asks for example, about the frequency and length of sessions, if an assessment was conducted and results revealed, what were the most and least helpful aspects of the consultations, and if the patient felt confidence in the psychologist's approach and methods.
It is hoped the survey will attract 2500 respondents within one year. It will be closed if this target is met earlier.
"We are as interested in the experiences of people who stopped treatment or changed practitioners as we are in those who attended all the sessions made available to them under the scheme," Honorary Professor Raj Sitharthan said.
"This research provides an important opportunity for those with first-hand experience of this federal mental health initiative to have their say," he said.
According to the 2007 National Survey of Mental Health and Wellbeing, almost 7.3 million Australians – around 45 per cent of the population aged between 16 and 85 years old – will experience a common mental health related condition such as depression, anxiety or a substance use disorder in their lifetime.
The Australian Institute of Health and Welfare reports mental health disorders are the leading cause of disability in the nation.
Honorary Professor Sitharthan said everyone pays a price for mental health.
"Whether the incidence of mental health conditions are associated with problems such as anxiety, depression or substance abuse, the social and economic costs to the individual, their family and friends, and the wider community can be enormous and long term.
"Our findings will help to develop more effective health care policies that better meet the needs of the community. They will also provide psychologists with valuable insights about their practices," he said.
The Better Mental Health Care initiative began in November 2006. It was introduced by the Commonwealth government to address the low treatment rates for high prevalence mental health disorders such as depression and anxiety, particularly for people presenting with mild to moderate severity where short term intervention is most likely to be useful.
This University of Sydney study, Expectations and Experiences of consumers seeking assistance from Psychologists/Clinical Psychologists, builds on earlier work by Dr Gomathi Sitharthan and Honorary Professor Raj Sitharthan. This has included investigating the expectations and experiences of people seeking assistance for substance abuse problems, improving doctor-patient communication, and clinical workforce training.
Interviews: Dr Gomathi Sitharthan t 9315 9584, [email protected]
Honorary Professor Raj Sitharthan m 0411 286 109, [email protected]
Media liaison: Jenny Eather m 0478 303 173, [email protected]