Bayesian Estimation of DSGE Models Edward P. Herbst and Frank Schorfheide
Description:
The focus of this course is the Bayesian estimation of DSGE models. Conditional on distributional assumptions for the exogenous shocks, the DSGE model generates a likelihood function, that is, a joint probability distribution for the endogenous model variables such as output, consumption, investment, and inflation that depends on the structural parameters of the model. These structural parameters characterize agents’ preferences, production technologies, and the law of motion of the exogenous shocks. In a Bayesian framework, this likelihood function can be used to transform a prior distribution for the structural parameters of the DSGE model into a posterior distribution. This posterior is the basis for substantive inference and decision making. Unfortunately, it is not feasible to characterize moments and quantiles of the posterior distribution analytically. Instead, we have to use computational techniques to generate draws from the posterior and then approximate posterior expectations by Monte Carlo averages.
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