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November 30-December 1, 2017 GCEP Masterclass taught by Giorgio Primiceri,...

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Georgetown University School of Continuing Studies

640 Massachusetts Avenue Northwest

C103 A/B

Washington, DC 20001

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November 30-December 1, 2017
Giorgio Primiceri, Northwestern University
Bayesian Inference in Macroeconomic Models

This course is an introduction to modern time series econometrics, with an emphasis on Bayesian methods to conduct inference in dynamic macroeconomic models. The two main subjects are vector autoregressions (VARs) and dynamic stochastic general equilibrium (DSGE) models, but we will touch upon several other topics, such as state-space models, Monte Carlo methods, model comparison and model choice.

VARs are very popular and flexible tools used for forecasting and the identification of economic shocks, representing a bridge between reduced-form and structural models. However, their flexibility comes at the cost of being very heavily parameterized. As a consequence, Bayesian inference is crucial to handle the proliferation of parameters and to improve dramatically their forecasting performance and the estimation accuracy of more structural objects (e.g. impulse responses).

The term DSGE model encompasses a broad class of macroeconomic models that spans the standard neoclassical growth model as well as New Keynesian monetary models with numerous shocks, real and nominal frictions. A common feature of these models is that decision rules of economic agents are derived from assumptions about preferences and technologies. Therefore, the DSGE paradigm delivers empirical models with a strong degree of theoretical coherence that are attractive for business cycle analysis and as laboratories for policy experiments. Bayesian techniques are widely employed for the estimation of DSGEs: prior distribution are used to add non-sample information, and posterior distributions summarize the uncertainty about model features, and can be efficiently evaluated with modern Bayesian computational tools.

The course is self-contained and does not assume prior knowledge of Bayesian inference. It is meant to be a gateway to the rapidly growing literature on modern macroeconometrics.

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Georgetown University School of Continuing Studies

640 Massachusetts Avenue Northwest

C103 A/B

Washington, DC 20001

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