A Jackknife Variance Estimator for Panel Regressions

Richard Crump, Nikolay Gospodinov and Ignacio Lopez Gaffney (iml2114@columbia.edu)

No 1133, Staff Reports from Federal Reserve Bank of New York

Abstract: 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.

Keywords: leave-one-out jackknife; Panel data model; strong time-series and cross-sectional dependence; cluster-robust variance estimation; trigonometric basis functions (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 C23 (search for similar items in EconPapers)
Pages: 46
Date: 2024-10-01
New Economics Papers: this item is included in nep-ecm
Note: Revised January 2025.
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Citations: View citations in EconPapers (1)

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DOI: 10.59576/sr.1133

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