Abstract and Keywords
This article presents the challenges that arise since macroeconomists often work in data-rich environments. It emphasizes multivariate models that can capture the co-movements of macroeconomic time series analysis. It discusses vector autoregressive (VAR) models distinguishing between reduced-form and structural VARs. Reduced-form VARs summarize the autocovariance properties of the data and provide a useful forecasting tool. The article shows how Bayesian methods have been empirically successful in responding to these challenges. It also encounters dynamic stochastic general equilibrium (DSGE) models that potentially differ in their economic implications. With posterior model probabilities, inference and decisions can be based on model averages. This article discusses inference with linearized as well as nonlinear DSGE models and reviews various approaches for evaluating the empirical fit of DSGE models. It concludes with a discussion of model uncertainty and decision-making with multiple models.
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