Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/309138 
Year of Publication: 
2025
Series/Report no.: 
Staff Reports No. 1133
Version Description: 
Revised January 2025
Publisher: 
Federal Reserve Bank of New York, New York, NY
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.
Subjects: 
leave-one-out jackknife
panel data models
strong time-series and cross-sectional dependence
cluster-robust variance estimation
trigonometric basis functions
JEL: 
C12
C13
C22
C23
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.