A New Jackknife Variance Estimator for Time-Series and Panel Regressions

Richard Crump, Nikolay Gospodinov and Ignacio Lopez Gaffney ()

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

Abstract: We introduce a new jackknife variance estimator for time-series and panel-data regressions. The novelty in our approach is that we first rotate the data using a particular choice of trigonometric basis functions. This rotation removes serial correlation in a broad class of time-series processes, including random walks, and enables the use of the conventional leave-one-out jackknife on the transformed space of the regressors and residuals. The procedure is tuning-parameter free and naturally adapts to the degree of persistence of the data. We prove the asymptotic validity of our variance estimator under general conditions and demonstrate excellent finite-sample properties in extensive simulation experiments, spanning a wide range of time-series and panel-data designs.

Keywords: long-run variance; HAC/HAR; leave-one-out jackknife; trigonometric basis functions; linear panel data models (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 C23 (search for similar items in EconPapers)
Pages: 77
Date: 2024-10-01
New Economics Papers: this item is included in nep-ecm
Note: Revised February 2026. Previous title: “A Jackknife Variance Estimator for Panel Regressions.”
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Citations: View citations in EconPapers (1)

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DOI: 10.59576/sr.1133

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