A Practical Approach to Testing Calibration Strategies

CAEPR Working Paper 2016-004

20 Pages Posted: 27 Sep 2016 Last revised: 4 Jan 2018

See all articles by Yongquan Cao

Yongquan Cao

Indiana University

Grey Gordon

Federal Reserve Banks - Federal Reserve Bank of Richmond

Date Written: January 3, 2018

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

JEL Classification: C13, C51, C52, C80, F34

Suggested Citation

Cao, Yongquan and Gordon, Grey, A Practical Approach to Testing Calibration Strategies (January 3, 2018). CAEPR Working Paper 2016-004, Available at SSRN: https://ssrn.com/abstract=2843814 or http://dx.doi.org/10.2139/ssrn.2843814

Yongquan Cao

Indiana University ( email )

100 South Woodlawn
Bloomington, IN 47405
United States

Grey Gordon (Contact Author)

Federal Reserve Banks - Federal Reserve Bank of Richmond ( email )

P.O. Box 27622
Richmond, VA 23261
United States

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