Inference in Dynamic Models for Panel Data Using The Moving Block Bootstrap

Ayden Higgins and Koen Jochmans
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Ayden Higgins: University of Exeter

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Abstract: Inference in linear panel data models is complicated by the presence of fixed effects when (some of) the regressors are not strictly exogenous. Under asymptotics where the number of cross-sectional observations and time periods grow at the same rate, the within-group estimator is consistent but its limit distribution features a bias term. In this paper we show that a panel version of the moving block bootstrap, where blocks of adjacent cross-sections are resampled with replacement, replicates the limit distribution of the within-group estimator. Confidence ellipsoids and hypothesis tests based on the reverse-percentile bootstrap are thus asymptotically valid without the need to take the presence of bias into account.

Keywords: Bootstrap; Dynamic model; Fixed effects; Inference; Asymptotic bias (search for similar items in EconPapers)
Date: 2025-02-14
Note: View the original document on HAL open archive server: https://hal.science/hal-04947761v1
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Working Paper: Inference in dynamic models for panel data using the moving block bootstrap (2025)
Working Paper: Inference in Dynamic Models for Panel Data Using The Moving Block Bootstrap (2025)
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