Bayesian Econometrics Research Articles

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Historical Developments in Bayesian Econometrics after Cowles Foundation Monographs 10, 14

After a brief description of the first Bayesian steps into econometrics in the 1960s and early 70s, publication and citation patterns are analyzed in ten major econometric journals until 2012. The results indicate that journals which contain both theoretical and applied papers, such as Journal of Econometrics, Journal of Business and Economic Statistics and Journal of Applied Econometrics, publish the large majority of high quality Bayesian econometric papers in contrast to theoretical journals like Econometrica and the Review of Economic Studies. These latter journals published, however, a few papers that had a substantial impact on Bayesian research. The journals Econometric Reviews and Econometric Theory published key invited papers and special issues that received wide attention, while Marketing Science shows an ever increasing number of papers since the middle n ineties. The International Economic Review and the Review of Economics and Statistics show a moderate time varying increase. The early nineties indicate an upward movement in publication patterns in most journals probably due to the effect of the ‘Computational Revolution'. Next, a visualization technique is used to connect papers and authors around important theoretical and empirical themes such as forecasting, macro models, marketing models, model uncertainty and sampling algorithms. The information distilled from this analysis shows the names of authors who contribute substantially to particular themes. This is followed by a discussion of those topics that pose interesting challenges for discussion amongst Bayesian econometricians, namely the computational revolution, unobserved component and flexible model structures, choice models, IV models, dynamic models and forecasting. Three issues are summarized where Bayesian and frequentist econometricians differ: Identification, the value of prior information and model evaluation; dynamic inference and nonstationarity; and vector autoregressive versus structural modeling. A major topic of debate amongst Bayesian econometricians is listed as objective versus subjective econometrics and communication problems and bridges between statistics and econometrics are summarized. The paper ends with a list of four important themes that will be a challenge for twenty-first century Bayesian econometrics: Sampling methods which are suitable for parallelization and GPU calculations, complex economic models which can account for nonlinearities, analysis of implied model features such as risk and instability and incorporating model incompleteness in econometric analysis.

Optimal Economic Policy and Oil Prices Shocks in Russia

The goal of the paper is to explain and analyze whether the Central Bank of Russia should include commodity prices into the lists of variables they try to respond. We augmented New Keynesian DSGE small open economy model of Dib (2008) with the oil stabilization fund and new Taylor-type monetary policy rule and estimated the model using Bayesian econometrics. The results show that Central Bank’s mild response to the oil price changes may be desired in terms of minimizing fluctuations of inflation and output only in the case when stabilization fund would be absent, while this response is redundant when“excess” oil revenues can be saved in the fund.

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Variable selection for market basket analysis

Results on cross category effects obtained by explanatory market basket analyses may be biased as studies typically investigate only a small fraction of the retail assortment (Chib et al. in Advances in econometrics, vol 16. Econometric models in marketing. JAI, Amsterdam, pp 57–92, 2002). We use Bayesian variable selection techniques to determine significant cross category effects in a multivariate logit model. Hence, we achieve a reduction of coefficients to be estimated which decreases computation time heavily and thus allows to consider more product categories than most previous studies. Next to the extension of numbers of categories, the second purpose of this paper is to learn about the capabilities of different variable selection algorithms in the context of market basket analysis. We present three different approaches to variable selection and find that an adaptation of a technique by Geweke (Contemporary Bayesian econometrics and statistics. Wiley, Hoboken, 2005) meets the requirements of market basket analysis best, namely high numbers of observations and cross category effects. For a real data set, we show (1) that only a moderate fraction of possible cross category effects are significantly different from zero (one third for our data), (2) that most of these effects indicate complementarity and (3) that the number of considered product categories influences significances of cross category effects.

Analysis of Business Cycles Asymmetries: A Minnesota Prior Approach

In this paper it was studied the cyclical and structural convergence between Romanian and Euro Zone's economies in a Minnesota Priors framework. For this purpose we called the Bayesian econometrics to estimate a Structural Vector Autoregression model SVAR with Gibbs Sampling. The methodological framework called in this paper is proposed by experts from the Federal Reserve Bank of Minnesota and from here the name of Minnesota Prior where a priori were introduced the hypothesis that endogenous variables follow different forms of a Markov first order autoregressive model, namely a Random Walk or an AR model. Focusing here especially on consumption cycles and using the relations provided by the theory, I was intended in study the asymmetries between supply and demand shocks in the case of Romanian and Euro Area economies. A higher correlation of shocks between the two studied economies indicates a structural similarity. In addition, for this purpose I involved several methods to ensure robustness checks in regard with the correlation of the two types of structural shocks.

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Measuring the natural output level by DSGE models: An empirical investigation for Switzerland

Summary The output gap plays an important role in the assessment and conduct of monetary policy. Most of the current literature, however, relies on filtering procedures which use ad hoc smoothness arguments for identification. Furthermore, they are subject to end-of-sample problems and do not provide estimates of the output gap based on economic theory. In contrast, our model-based approach relies on a precise definition of the natural level of output and consequently of the output gap. This paper provides, for the first time, an estimate of the output gap based on a DSGE small open economy model for Switzerland. We use Bayesian econometrics to derive an estimate of the output gap, which is then compared to some alternatives. Except for the last years, our measure of the output gap is close to zero most of the time. This suggests that price rigidities play a minor role in the propagation of the Swiss business cycle. We show that our estimate produces sensible and robust results which encourage further research.

Introduction to Bayesian Econometrics

Economic RecordVolume 85, Issue 270 p. 364-366 Introduction to Bayesian Econometrics Liana Jacobi, Liana Jacobi Department of EconomicsThe University of MelbourneSearch for more papers by this author Liana Jacobi, Liana Jacobi Department of EconomicsThe University of MelbourneSearch for more papers by this author First published: 18 August 2009 https://doi.org/10.1111/j.1475-4932.2009.00578.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Volume85, Issue270September 2009Pages 364-366 RelatedInformation

Prognozowanie w ramach bayesowskich modeli VEC

The majority of the observed macroeconomic time series may contain stochastic trends or unit roots. After the introduction of the conception of the cointegration (which enables proper modelling of such time series) an enormous development of methods of its analysis can be observed in classical as well in Bayesian econometrics. The aim of this paper is to present a Bayesian approach to prediction in the framework of the VEC model. The presented methods will be applied to the forecast of the Polish inflation and the rate of unemployment preceded by the analysis of the price – wage mechanism.

Two Books on the New Macroeconometrics

Methods for Applied Macroeconomics Research by Fabio Canova, and Structural Macroeconometrics by David N. DeJong and Chetan Dave are two outstanding new books that provide an excellent introduction to what is sometimes called the New Macroeconometrics. This area of empirical macroeconomics is centered on the estimation and validation of dynamic stochastic general equilibrium (DSGE) models. Canova's and DeJong and Dave's volumes fill a tremendous gap in economists' libraries. Not only does the writing style of both books allow them to be adopted as a reference text for a class, but also the books come filled with applications, exercises, and pointers to computer code that will complement the lectures. Despite sharing the common theme of an introduction to the new macroeconometrics, each book has its own focus. Canova's book aims to survey a long list of techniques relevant to macroeconomists: filters, vector autoregressions (VARs), general method of moments (GMM), simulation methods, dynamic panels, maximum likelihood, and Bayesian econometrics; it also offers two preliminary chapters on probability theory and on DSGE modeling. In contrast, DeJong and Dave have the more modest goal of showing how to compute and estimate DSGE models, which makes it more suitable for a second year graduate class. In exchange, DeJong and Dave often dig a bit deeper into issues of interest to them and build the material at a more leisurely pace.

John F. Geweke (2005): Contemporary Bayesian econometrics and statistics (Wiley series in probability and statistics)

John F. Geweke (2005): Contemporary Bayesian econometrics and statistics (Wiley series in probability and statistics)

Testing for market power in the Australian grains and oilseeds industries

AbstractWe formally assess competitive buying and selling behavior in the Australian grains and oilseeds industries using a more realistic empirical model and a less aggregated data set than previously available. We specify a duality model of profit maximization that allows for imperfect competition in both input and output markets and for variable‐proportions technologies. Aggregate input‐output data are used to define the structure of the relevant industries, and time series data are then used to implement the model for 13 grains and oilseeds products handled by seven groups of agents. The model is estimated in a Bayesian econometrics framework. We find evidence of flour and cereal food product manufacturers exerting market power when purchasing wheat, barley, oats and triticale; beer and malt manufacturers exerting market power when purchasing wheat and barley; and other food product manufacturers exerting market power when purchasing wheat, barley, oats and triticale. [EconLit citations: C11, L66, Q11]. © 2007 Wiley Periodicals, Inc. Agribusiness 23: 349–376, 2007.

Editors' Introduction to the Special Issue of Econometric Reviews on Bayesian Dynamic Econometrics

In the late 1970s and 1980s, a series of influential papers (e.g., Geweke, 1989; Kloek and van Dijk, 1978) introduced and developed methods of posterior simulation that established Bayesian econome...

Bayesian Model Comparison and Validation

Bayesian econometrics provides a tidy theory and practical methods of comparing and combining several alternative, completely specified models for a common dataset, which is reviewed in Section I. Dale J. Poirier (1988) provides a more extensive introduction. It is always possible that none of the specified models describes important aspects of the data well. The investigation of this possibility, a process known as model validation or model specification checking, is an important part of applied econometric work. Bayesian theory and practice for model validation are developed less well. A well-established Bayesian literature, beginning with George E. P. Box (1980) and summarized in Roderik J. Little (2006), argues that non-Bayesian methods are essential in model validation. This line of thought persists in Bayesian econometrics as well, (see recent texts by Tony Lancaster 2004 and John Geweke 2005). Section II reviews these methods. This essay suggests an alternative, fully Bayesian method of model validation based on the concept of incomplete models, introduced in Section III. The concluding section of this essay argues that this method is also strategically advantageous in applied Bayesian econometrics.

FROM UNCERTAINTY TO MACROECONOMICS AND BACK: AN INTERVIEW WITH JACQUES DRÈZE

Jacques Dreze was born in Verviers, Belgium, in 1929 and completed his Ph.D. in Economics in 1958 at Columbia. His contribution to economics is exceptional, opening up new paths of research in various areas including general equilibrium, decision theory, game theory, econometrics (in particular, Bayesian econometrics), followed by contributions to macroeconomics and economic policy. Dreze has been president of the Econometric Society as well as associate editor and co-editor of Econometrica; founding member and first president of the European Economic Association; president of the International Economic Association and honorary member of the American Economic Association and the National Academy of Sciences. He has received 15 honorary doctorates, including one from the University of Chicago. He has engaged in the promotion and development of research and higher education in Europe, being a founding member of the Centre for Operations Research and Econometrics (CORE) and of the European Doctoral Program in Quantitative Economics (EDP). He has also actively participated in the most pressing economic problems in Europe. The interview mainly aims at inviting Jacques Dreze to explain what have been his major contributions and what avenues he suggests for future research in macroeconomics. The interview has been conducted sequentially. It started in October 2002 at the European University Institute. The long material collected along these highly illuminating talks was polished and condensed in the following pages. They are organized in the following way. The first section refers to Jacques' initial experiences in Belgium followed by his study of economics in the United States. The second section is devoted to the unity underlying an apparent diversity in his research interests. The third section is devoted to his more recent contributions to macroeconomics, which may be stated as “incomplete markets drive multiplicity of equilibria, calling for active economic policies.” The interview concludes with his contribution to the promotion of research and, in particular, research institutions across Europe. Both Pierre Dehez and Omar Licandro were students of Jacques Dreze. Dehez and Dreze co-authored several papers, some of which are mentioned in the interview.

Book review

Bayesian Econometrics By Gary Koop.