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Structural estimation of jump-diffusion processes in macroeconomics

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  • Olaf Posch

    ()
    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

Abstract

Understanding the process of economic growth involves comparing competing theoretical models and evaluating their empirical relevance. Our approach is to take the neoclassical stochastic growth model directly to the data and make inferences about the model parameters of interest. In this paper, output follows a jump-diffusion process. By imposing parameter restrictions we derive two solutions in explicit form. Based on them, we obtain transition densities in closed form and employ maximum likelihood techniques to estimate the model parameters. In extensive Monte Carlo simulations we demonstrate that population parameters of the underlying data generating process can be recovered. We find empirical evidence for jumps in monthly and quarterly data on industrial production for the UK, the US, Germany, and the euro area (Euro12).

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2007-23.

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Length: 59
Date of creation: 14 Sep 2007
Date of revision:
Handle: RePEc:aah:create:2007-23

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Web page: http://www.econ.au.dk/afn/

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Keywords: Jump-diffusion estimation; Stochastic growth; Closed form solutions;

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