Table of Contents

Handbook of Research Methods and Applications in Empirical Macroeconomics

Handbook of Research Methods and Applications in Empirical Macroeconomics

Handbooks of Research Methods and Applications series

Edited by Nigar Hashimzade and Michael A. Thornton

This comprehensive Handbook presents the current state of art in the theory and methodology of macroeconomic data analysis. It is intended as a reference for graduate students and researchers interested in exploring new methodologies, but can also be employed as a graduate text. The Handbook concentrates on the most important issues, models and techniques for research in macroeconomics, and highlights the core methodologies and their empirical application in an accessible manner. Each chapter is largely self-contained, whilst the comprehensive introduction provides an overview of the key statistical concepts and methods. All of the chapters include the essential references for each topic and provide a sound guide for further reading.

Chapter 16: Bayesian methods

Luc Bauwens and Dimitris Korobilis

Subjects: economics and finance, econometrics, research methods, research methods in economics


The scope of this chapter is to introduce applied macroeconomists to the world of Bayesian estimation methods. Why would an empirical macroeconomist invest in learning Bayesian estimation after having invested hours learning estimation methods like maximum likelihood and generalized method of moments (see previous chapters)? Nowadays, with the advancement of computing power and the establishment of new simulation techniques, it is probably much easier to answer this question compared to, say, thirty years ago. First, Bayesian methods offer a wide range of estimation tools for macroeconomic models, ranging from simple time series models to structural macroeconometric models. When optimizing the likelihood function becomes a daunting task (due to its high dimensionality or multimodality, or due to underidentification of specific model parameters), Bayesian methods can prove more robust since they do not rely on using complex optimization techniques that might get stuck in local optima. Second, Bayesian methods allow the researcher to incorporate prior beliefs in her model. We argue in this chapter that such beliefs are not so difficult to formalize as one may fear a priori, and that they may help to get reasonable estimates and forecasts, for example through soft shrinkage constraints.

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