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Inference in dynamic models for panel data using the moving block bootstrap

Ayden Higgins, Koen Jochmans

arXiv 12 Feb 2025 · Econometrics · 1 citations (OpenAlex)

arXiv:2502.08311 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

23
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47
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3,358
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Gon calves, S (2011) The moving blocks bootstrap for panel linear regression models with individual effects0.73732100%
2Künsch, H. R (1989) The jackknife and the bootstrap for general stationary observations0.73732100%
3Nickell, S (1981) Biases in dynamic models with fixed effects0.64422100%
4Gon calves, S. and M. Kaffo (2015) Bootstrap inference for linear dynamic panel data models with individual fixed effects0.58531100%
5Higgins, A. and K. Jochmans (2024) Bootstrap inference for fixed-effect models self0.51121100%
6Arellano, M (2003) Panel Data Econometrics0.40511100%
7Arellano, M. and S. Bond (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations0.40511100%
8Arellano, M. and J. Hahn (2007) Understanding bias in nonlinear panel models: Some recent developments0.40511100%
9Cavaliere, G., S. Gon calves, M. . Nielsen, and E. Zanelli (2024) Bootstrap inference in the presence of bias0.40511100%
10Chudik, A., M. H. Pesaran, and J.-C. Yang (2018) Half-panel jackknife fixed-effects estimation of linear panels with weakly exogenous regressors0.40511100%

Showing the top 10 of 23 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Jackknife Inference for Fixed Effects Models0.73732
2Bootstrap Inference in Nonlinear Panel Data Models with Interactive Fixed Effects0.64422
32507.164620.51121