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Unobserved Grouped Heteroskedasticity and Fixed Effects

Jorge A. Rivero

arXiv 21 Oct 2023 · Econometrics

arXiv:2310.14068 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper extends the linear grouped fixed effects (GFE) panel model to allow for heteroskedasticity from a discrete latent group variable. Key features of GFE are preserved, such as individuals belonging to one of a finite number of groups and group membership is unrestricted and estimated. Ignoring group heteroskedasticity may lead to poor classification, which is detrimental to finite sample bias and standard errors of estimators. I introduce the "weighted grouped fixed effects" (WGFE) estimator that minimizes a weighted average of group sum of squared residuals. I establish $\sqrt{NT}$-consistency and normality under a concept of group separation based on second moments. A test of group homoskedasticity is discussed. A fast computation procedure is provided. Simulations show that WGFE outperforms alternatives that exclude second moment information. I demonstrate this approach by considering the link between income and democracy and the effect of unionization on earnings.

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66
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113
in-text mentions
66
distinct cited
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13,150
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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
1S. Bonhomme and E. Manresa (2015) Grouped patterns of heterogeneity in panel data0.87424567%
2H. Yang and E. G. Tabak (2022) Conditional density estimation, latent variable discovery, and optimal transport0.8434375%
3J. Bai and S. Ng (2002) Determining the number of factors in approximate factor models0.84333100%
4D. Card (1996) The effect of unions on the structure of wages: A longitudinal analysis0.81142100%
5R. B. Freeman (1984) Longitudinal analyses of the effects of trade unions0.81142100%
6D. Acemoglu, S. Johnson, J. A. Robinson, and P. Yared (2008) Income and democracy0.73732100%
7J. Aguilar and T. Boot (2022) Grouped heterogeneity in linear panel data models with heterogeneous error variances0.73732100%
8J. Kim and L. Wang (2019) Hidden group patterns in democracy developments: Bayesian inference for grouped heterogeneity0.73732100%
9S. Lloyd (1982) Least squares quantization in PCM0.73732100%
10E. W. Forgy (1965) Cluster analysis of multivariate data: efficiency versus interpretability of classifications0.64422100%

Showing the top 10 of 66 scored citations.