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Forecasting with estimated dynamic stochastic general equilibrium models: The role of nonlinearities

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Abstract

In this paper we study the effects of nonlinearities on the forecasting performance of a dynamic stochastic general equilibrium model. We compute first- and second-order approximations to a New Keynesian monetary model, and use artificial data to estimate the model's structural parameters based on its linear and quadratic solution. We and that, although our model in not far from being linear, the forecasting performance improves by capturing the second-order terms in the solution. Our findings suggest that accounting for nonlinearities will improve the predictive abilities of DSGE models in many applications.

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  • Paul Pichler, 2007. "Forecasting with estimated dynamic stochastic general equilibrium models: The role of nonlinearities," Vienna Economics Papers vie0702, University of Vienna, Department of Economics.
    • Handle: RePEc:vie:viennp:vie0702
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    More about this item

    JEL classification:

    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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