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Staff Reports

A Jackknife Variance Estimator for Panel Regressions

Number 1133
October 2024

JEL classification: C12, C13, C22, C23

Authors: Richard K. Crump, Nikolay Gospodinov, and Ignacio Lopez Gaffney

We introduce a new jackknife variance estimator for panel-data regressions. Our variance estimator can be motivated as the conventional leave-one-out jackknife variance estimator on a transformed space of the regressors and residuals using orthonormal trigonometric basis functions. We prove the asymptotic validity of our variance estimator and demonstrate desirable finite-sample properties in a series of simulation experiments. We also illustrate how our method can be used for jackknife bias-correction in a variety of time-series settings.

Full Article

Author Disclosure Statement(s)

Richard K. Crump
I declare that I have no relevant or material financial interests that relate to the research described in my paper entitled “A Jackknife Variance Estimator for Panel Regressions,” joint with Nikolay Gospodinov and Ignacio Lopez Gaffney.

Nikolay Gospodinov
I declare that I have no relevant or material financial interests that relate to the research described in my paper entitled “A Jackknife Variance Estimator for Panel Regressions,” joint with Richard Crump and Ignacio Lopez Gaffney.

Ignacio Lopez Gaffney
I declare that I have no relevant or material financial interests that relate to the research described in my paper entitled “A Jackknife Variance Estimator for Panel Regressions” joint with Richard Crump and Nikolay Gospodinov.

Suggested Citation:
Crump, Richard K., Nikolay Gospodinov, and Ignacio Lopez Gaffney. 2024. “A Jackknife Variance Estimator for Panel Regressions.” Federal Reserve Bank of New York Staff Reports, no. 1133, October. https://doi.org/10.59576/sr.1133

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