Intended for |
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Practitioners, researchers, academics and applied macroeconomists with an interest in estimating Dynamic Stochastic General Equilibrium (DSGE) models. |
Prerequisites |
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First-year graduate level knowledge about dynamic programming and basic familiarity with MATLAB and DYNARE will be useful to follow the class and hands-on computer sessions. |
Overview |
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The course will provide an in-depth study of methods to estimate DSGE models. We will use the canonical New Keynesian model as a workhorse model during the course. |
Topics |
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Brief review of the New Keynesian model
Structural model estimation using full information (Bayesian) estimation techniques
- Likelihood, Kalman filter, Priors, Posteriors, Monte Carlo Markov Chains (MCMC), etc.
Structural model estimation using limited information (Bayesian) estimation techniques
- (Bayesian) impulse response matching, (Bayesian) moment matching
Hands-on MATLAB and DYNARE sessions
- Full information Bayesian maximum likelihood estimation of the New Keynesian model
- Limited information Bayesian impulse response matching of the New Keynesian model with an identified monetary policy shock of a structural vector autoregression
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Prof. Trabandt holds the Chair of Macroeconomics at the School of Business and Economics at Freie Universität Berlin. He was previously Chief of the Global Modeling Studies Section at the International Finance Division of the Federal Reserve Board of Governors in Washington D.C. His research has been published in the American Economic Journal: Macroeconomics, Econometrica, the Journal of Applied Econometrics, the Journal of Economic Dynamics and Control, the Journal of Monetary Economics, the European Economic Review, the American Economic Review (Papers and Proceedings) and the Handbook of Monetary Economics. |
