A Practical Approach to Testing Calibration Strategies

Yongquan Cao () and Grey Gordon
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Yongquan Cao: Indiana University

No 2016-004, CAEPR Working Papers from Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington

Abstract: A calibration strategy tries to match target moments using a model's parameters. We propose tests for determining whether this is possible. The tests use moments at random parameter draws to assess whether the target moments are similar to the computed ones (evidence of existence) or appear to be outliers (evidence of non-existence). Our experiments show the tests are effective at detecting both existence and non-existence in a non-linear model. Multiple calibration strategies can be quickly tested using just one set of simulated data. Applying our approach to indirect inference allows for the testing of many auxiliary model specifications simultaneously. Code is provided.

Keywords: Calibration; GMM; Indirect Inference; Existence, Misspecification; Outlier Detection; Data Mining (search for similar items in EconPapers)
JEL-codes: C13 C51 C52 C80 F34 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2016-09, Revised 2018-01
New Economics Papers: this item is included in nep-ecm
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Journal Article: A Practical Approach to Testing Calibration Strategies (2019)
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