MANYWEAKIV: Stata module to implement the weak-identification robust jackknife AR test from Mikusheva and Sun (2022)

Liyang Sun

Statistical Software Components from Boston College Department of Economics

Abstract: In empirical applications using instrumental variables, the current consensus practice is to report the first stage F statistic and as long as it is above 10, researchers are allowed to rely on standard t-statistics inferences. This practice has foundations in Stock and Yogo (2005) which showed that the concentration parameter fully characterizes the size distortion of the TSLS-Wald test, and empirically the concentration parameter can be judged based on the first stage F statistics. This result has been obtained under the assumptions of homoscedasticity and for a fixed number of instruments. Mikusheva and Sun (2022) introduces a new F test that is valid under heteroscedasticity and many instruments. Based on the result of this new F test (implemented in manyweakpretest), applied researchers can switch between the 5% JIVE t-statistic or 5% jackknife AR test (implemented in manyweakivtest) with the caveats analogous to Stock and Yogo (2005): Namely, the size of the two-step procedure are bounded within 15%.

Language: Stata
Requires: Stata version 13
Keywords: weak identification; robust jackknife; AR test (search for similar items in EconPapers)
Date: 2023-11-18
Note: This module should be installed from within Stata by typing "ssc install manyweakiv". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/m/manyweakivtest.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/m/manyweakivpretest.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/m/manyweakiv.sthlp help file (text/plain)

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