Perturbations in DSGE Models: Odd Derivatives Theorem

Sherwin Lott ()
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Sherwin Lott: Department of Economics, University of Pennsylvania

PIER Working Paper Archive from Penn Institute for Economic Research, 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.

Keywords: Perturbation methods; DSGE models; odd derivatives; computational macroeconomics (search for similar items in EconPapers)
Pages: 20 pages
Date: 2018-05-21, Revised 2018-05-21
New Economics Papers: this item is included in nep-cmp and nep-dge
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:18-011

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