Handbook of Research Methods and Applications in Empirical Macroeconomics
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Handbook of Research Methods and Applications in Empirical Macroeconomics

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.
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Chapter 21: Bayesian estimation of DSGE models

Pablo A. Guerrón-Quintana and James M. Nason


Macroeconomists have made substantial investments in Bayesian time series during the last 30 years. One reason is that Bayesian methods afford researchers the chance to estimate and evaluate a wide variety of macro models that frequentist econometrics often find challenging. Bayesian vector autoregressions (BVARs) represent an early return on this research project manifested, for example, by Doan et al. (1984). They show that BVARs are useful forecasting tools. More recent work focuses on developing Bayesian methods capable of estimating time-varying parameter (TVP) VARs, associated with Cogley and Sargent (2005) and Primiceri (2005), and Markov-switching (MS) VARs initiated by Sims and Zha (2006). The complexity of TVP- and MS-VARs underlines the efforts macroeconomists have put into developing useful Bayesian time series tools. Bayesian times series methods are also attractive for macroeconomists studying dynamic stochastic general equilibrium (DSGE) models. Although DSGE models can be estimated using classical optimization methods, macroeconomists often prefer to use Bayesian tools for these tasks. One reason is that advances in Bayesian theory are providing an expanding array of tools that researchers can employ to estimate and evaluate DSGE models. The popularity of the Bayesian approach is also explained by the increasing computational power available to estimate and evaluate medium-to large-scale DSGE models using Markov chain Monte Carlo (MCMC) simulators. These DSGE models can pose identification problems for frequentist estimators that no amount of data or computing power can overcome.

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