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The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications / Martin M. Andreasen, Jesús Fernández-Villaverde, Juan Rubio-Ramírez.

By: Contributor(s): Material type: TextTextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w18983.Publication details: Cambridge, Mass. National Bureau of Economic Research 2013.Description: 1 online resource: illustrations (black and white)Subject(s): Online resources: Available additional physical forms:
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Abstract: This paper studies the pruned state-space system for higher-order approximations to the solutions of DSGE models. For second- and third-order approximations, we derive the statistical properties of this system and provide closed-form expressions for first and second unconditional moments and impulse response functions. Thus, our analysis introduces GMM estimation for DSGE models approximated up to third-order and provides the foundation for indirect inference and SMM when simulation is required. We illustrate the usefulness of our approach by estimating a New Keynesian model with habits and Epstein-Zin preferences by GMM when using first and second unconditional moments of macroeconomic and financial data and by SMM when using additional third and fourth unconditional moments and non-Gaussian innovations.
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April 2013.

This paper studies the pruned state-space system for higher-order approximations to the solutions of DSGE models. For second- and third-order approximations, we derive the statistical properties of this system and provide closed-form expressions for first and second unconditional moments and impulse response functions. Thus, our analysis introduces GMM estimation for DSGE models approximated up to third-order and provides the foundation for indirect inference and SMM when simulation is required. We illustrate the usefulness of our approach by estimating a New Keynesian model with habits and Epstein-Zin preferences by GMM when using first and second unconditional moments of macroeconomic and financial data and by SMM when using additional third and fourth unconditional moments and non-Gaussian innovations.

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