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Perturbations in DSGE Models: Odd Derivatives Theorem

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  • Sherwin Lott

    (Department of Economics, University of Pennsylvania)

Abstract

When testing a theory, we should ask not just whether its predictions match what we see in the data, but also about its “completeness†: how much of the predictable variation in the data does the theory capture? Deï¬ ning completeness is conceptually challenging, but we show how methods based on machine learning can provide tractable measures of completeness. We also identify a model domain—the human perception and generation of randomness—where measures of completeness can be feasibly analyzed; from these measures we discover there is signiï¬ cant structure in the problem that existing theories have yet to capture.

Suggested Citation

  • Sherwin Lott, 2018. "Perturbations in DSGE Models: Odd Derivatives Theorem," PIER Working Paper Archive 18-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 21 May 2018.
    • Handle: RePEc:pen:papers:18-011
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    References listed on IDEAS

    as
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    Keywords

    Perturbation methods; DSGE models; odd derivatives; computational macroeconomics;
    All these keywords.

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