Forecast Combination and Bayesian Model Averaging - A Prior Sensitivity Analysis

In this study the forecast performance of model averaged forecasts is compared to that of alternative single models. Following Eklund and Karlsson (2007) we form posterior model probabilities - the weights for the combined forecast - based on the predictive likelihood. Extending the work of Fernández et al. (2001a) we carry out a prior sensitivity analysis for a key parameter in Bayesian model averaging (BMA): Zellner's g. The main results based on a simulation study are fourfold: First the predictive likelihood does always better than the traditionally employed 'marginal' likelihood in settings where the true model is not part of the model space. Secondly, and more striking, forecast accuracy as measured by the root mean square error (rmse) is maximized for the median probability model put forward by Barbieri and Berger (2003). On the other hand, model averaging excels in predicting direction of changes, a finding that is in line with Crespo Cuaresma (2007). Lastly, our recommendation concerning the prior on g is to choose the prior proposed by Laud and Ibrahim (1995) with a hold-out sample size of 25% to minimize the rmse (median model) and 75% to optimize direction of change forecasts (model averaging). We finally forecast the monthly industrial production output of six Central Eastern and South Eastern European (CESEE) economies for a one step ahead forecasting horizon. Following the aforementioned forecasting recommendations improves the out-of-sample statistics over a 30-period horizon beating for almost all countries the first order autoregressive benchmark model.Forecast Combination; Bayesian Model Averaging; Median Probability Model; Predictive Likelihood; Industrial Production; Model Uncertainty

Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?

We assess the relationship between model size and complexity in the time-varying parameter VAR framework via thorough predictive exercises for the Euro Area, the United Kingdom and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets while simpler models tend to perform better in sizeable data sets. To combine best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time

How does monetary policy affect income inequality in Japan? Evidence from grouped data

We examine the effects of monetary policy on income inequality in Japan using a novel econometric approach that jointly estimates the Gini coefficient based on micro-level grouped data of households and the dynamics of macroeconomic quantities. Our results indicate different effects on income inequality for different types of households: A monetary tightening increases inequality when income data is based on households whose head is employed (workers' households), while the effect reverses over the medium term when considering a broader definition of households. Differences in the relative strength of the transmission channels can account for this finding. Finally we demonstrate that the proposed joint estimation strategy leads to more informative inference while results based on the frequently used two-step estimation approach yields inconclusive results.Series: Working Papers in Regional Scienc

The impact of data revisions on the robustness of growth determinants: A note on 'determinants of economic growth: will data tell?'

Ciccone and Jarocínski (2010) show that inference in Bayesian model averaging (BMA) can be highly sensitive to small changes in the dependent variable. In particular they demonstrate that the importance of growth determinants in explaining growth varies tremendously over different revisions of Penn World Table (PWT) income data. They conclude that 'agnostic' priors appear too sensible for this strand of growth empirics. In response, we show that the instability found owes much to a specific BMA set-up: the variation in results can be considerably reduced by applying an evenly 'agnostic', but exible prior

Unconventional US Monetary Policy: New Tools, Same Channels?

In this paper we compare the transmission of a conventional monetary policy shock with that of an unexpected decrease in the term spread, which mirrors quantitative easing. Employing a time-varying vector autoregression with stochastic volatility, our results are two-fold: First, the spread shock works mainly through a boost to consumer wealth growth, while a conventional monetary policy shock affects real output growth via a broad credit / bank lending channel. Second, both shocks exhibit a distinct pattern over our sample period. More specifically, we find small output effects of a conventional monetary policy shock during the period of the global financial crisis and stronger effects in its aftermath. This might imply that when the central bank has left the policy rate unaltered for an extended period of time, a policy surprise might boost output particularly strongly. By contrast, the spread shock has affected output growth most strongly during the period of the global financial crisis and less so thereafter. This might point to diminishing effects of large scale asset purchase programs. (authors' abstrct)Series: Department of Economics Working Paper Serie

The Determinants of Economic Growth in European Regions

We use Bayesian Model Averaging (BMA) to evaluate the robustness of determinants of economic growth in a new dataset of 255 European regions in the 1995-2005 period. We use three different specifications based on (1) the cross-section of regions, (2) the cross-section of regions with country fixed effects and (3) the cross-section of regions with a spatial autoregressive (SAR) structure. We investigate the existence of parameter heterogeneity by allowing for interactions of potential explanatory variables with geographical dummies as extra regressors. We find remarkable differences between the determinants of economic growth implied by differences between regions and those within regions of a given country. In the cross-section of regions, we find evidence for conditional convergence with speed around two percent. The convergence process between countries is dominated by the catching up process of regions in Central and Eastern Europe (CEE), whereas convergence within countries is mostly a characteristic of regions in old EU member states. We also find robust evidence of positive growth of capital cities, a highly educated workforce and a negative effect of population density.model uncertainty, spatial autoregressive model, determinants of economic growth, European regions

Spillovers from US monetary policy: Evidence from a time-varying parameter global vector autoregressive model

The paper develops a global vector auto-regressive model with time varying pa- rameters and stochastic volatility to analyse whether international spillovers of US monetary policy have changed over time. The model proposed enables us to assess whether coefficients evolve gradually over time or are better characterized by infrequent, but large, breaks. Our find- ings point towards pronounced changes in the international transmission of US monetary policy throughout the sample period, especially so for the reaction of international output, equity prices and exchange rates against the US dollar. In general, the strength of spillovers has weakened in the aftermath of the global financial crisis. Using simple panel regressions, we link the vari- ation in international responses to measures of trade and financial globalization. We find that a broad trade base and a high degree of financial integration with the world economy tend to cushion risks stemming from a foreign shock such as US tightening of monetary policy, whereas a reduction in trade barriers and/or a liberalization of the capital account increase these risks

Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R

This article describes the BMS (Bayesian model sampling) package for R that implements Bayesian model averaging for linear regression models. The package excels in allowing for a variety of prior structures, among them the "binomial-beta" prior on the model space and the so-called "hyper-g" specifications for Zellner's g prior. Furthermore, the BMS package allows the user to specify her own model priors and offers a possibility of subjective inference by setting "prior inclusion probabilities" according to the researcher's beliefs. Furthermore, graphical analysis of results is provided by numerous built-in plot functions of posterior densities, predictive densities and graphical illustrations to compare results under different prior settings. Finally, the package provides full enumeration of the model space for small scale problems as well as two efficient MCMC (Markov chain Monte Carlo) samplers that sort through the model space when the number of potential covariates is large

Adaptive Shrinkage in Bayesian Vector Autoregressive Models

Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis. For both applications, shrinkage priors can help improving inference. In this paper we derive the shrinkage prior of Griffin et al. (2010) for the VAR case and its relevant conditional posterior distributions. This framework imposes a set of normally distributed priors on the autoregressive coefficients and the covariances of the VAR along with Gamma priors on a set of local and global prior scaling parameters. This prior setup is then generalized by introducing another layer of shrinkage with scaling parameters that push certain regions of the parameter space to zero. A simulation exercise shows that the proposed framework yields more precise estimates of the model parameters and impulse response functions. In addition, a forecasting exercise applied to US data shows that the proposed prior outperforms other specifications in terms of point and density predictions. (authors' abstract)Series: Department of Economics Working Paper Serie

How does monetary policy affect income inequality in Japan? Evidence from grouped data

We examine the effects of monetary policy on income inequality in Japan using a novel econometric approach that jointly estimates the Gini coefficient based on micro-level grouped data of households and the dynamics of macroeconomic quantities. Our results indicate different effects on income inequality for different types of households: A monetary tightening increases inequality when income data is based on households whose head is employed (workers' households), while the effect reverses over the medium term when considering a broader definition of households. Differences in the relative strength of the transmission channels can account for this finding. Finally we demonstrate that the proposed joint estimation strategy leads to more informative inference while results based on the frequently used two-step estimation approach yields inconclusive results.Comment: 25 page