Matlab function 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: Matlab R2023a on Windows 10 PC (64-bit)
Other versions: Python
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example.m: Simple example illustrating the main functionality of the package step by step
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@MinDist: Matlab class for minimum distance estimation, standard errors, and testing
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application/run_all.m: Application and simulation study based on Alvarez & Lippi (2014), see top of file for data requirements
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tests: Unit tests
For some functionality (such as joint testing) it is necessary to install the cvx Matlab package.
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.