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Standard errors for moment matching estimators given limited knowledge about the moment variance-covariance matrix

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mikkelpm/stderr_calibration_python

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Standard Errors for Calibrated Parameters

Python package that computes worst-case standard errors (SE) for minimum distance estimators, given knowledge of only the marginal variances (but not correlations) of the matched moments.

The computed worst-case SE for the estimated parameters are sharp upper bounds on the true SE (which depend on the unknown moment correlation structure). For over-identified models, the package also computes the efficient moment selection that minimizes the worst-case SE. Additionally, the package can carry out tests of parameter restrictions or over-identifying restrictions.

Reference: Cocci, Matthew D., and Mikkel Plagborg-Møller (2023), "Standard Errors for Calibrated Parameters", arXiv:2109.08109

Tested in: Python 3.8.18 (Anaconda distribution) on Windows 10 PC with NumPy version 1.24.3.

Other versions: Matlab

Contents

Requirements

The Python packages cvxopt and numdifftools are required.

Acknowledgements

We thank Minsu Chang and Silvia Miranda-Agrippino for supplying the moments used in the empirical application.

This material is based upon work supported by the NSF under Grant #1851665. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.