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A Jackknife Variance Estimator for Panel Regressions

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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.

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  • Richard K. Crump & Nikolay Gospodinov & Ignacio Lopez Gaffney, 2024. "A Jackknife Variance Estimator for Panel Regressions," Staff Reports 1133, Federal Reserve Bank of New York.
    • Handle: RePEc:fip:fednsr:99064
    DOI: 10.59576/sr.1133
    Note: Revised January 2025.
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    1. Richard K. Crump & Nikolay Gospodinov & Ignacio Lopez Gaffney, 2024. "A Simple Diagnostic for Time-Series and Panel-Data Regressions," Staff Reports 1132, Federal Reserve Bank of New York.

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    More about this item

    Keywords

    leave-one-out jackknife; Panel data model; strong time-series and cross-sectional dependence; cluster-robust variance estimation; trigonometric basis functions;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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