journal article Jan 01, 2007

Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects

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Abstract
This paper suggests a three-stage procedure for the estimation of time-invariant and rarely changing variables in panel data models with unit effects. The first stage of the proposed estimator runs a fixed-effects model to obtain the unit effects, the second stage breaks down the unit effects into a part explained by the time-invariant and/or rarely changing variables and an error term, and the third stage reestimates the first stage by pooled OLS (with or without autocorrelation correction and with or without panel-corrected SEs) including the time-invariant variables plus the error term of stage 2, which then accounts for the unexplained part of the unit effects. We use Monte Carlo simulations to compare the finite sample properties of our estimator to the finite sample properties of competing estimators. In doing so, we demonstrate that our proposed technique provides the most reliable estimates under a wide variety of specifications common to real world data.
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References
39
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This has been suggested by Amemiya and MaCurdy (1986), Breusch, Mizon, and Schmidt (1989), Baltagi and Khanti-Akom (1990), Baltagi, Bresson, and Pirotte (2003), and Oaxaca and Geisler (2003).
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We also varied the number of units (N = 15, 30, 50, 70, 100) and the number of time periods (T = 20, 40, 70, 100). We report these results only in the online appendix. The number of possible permutations of these settings is 2000 that would have led to 2000 times the aggregated number of estimators used in both experiments times 1000 single estimations in the Monte Carlo analyses. In total, this would have given 18 million regressions. However, without loss of generality, we simplified the Monte Carlos and estimated “only” 980,000 single regression models.
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We follow standard practice by this notation. However, from equation (4) it follows that the FE estimate of the unit effects propels much more to the estimated unit effects. To avoid confusion and maintain consistence with standard textbooks, we stick to this notation—needless to say that it does not make much sense.
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None of the three main textbooks on panel data analysis (Baltagi 2001; Wooldridge 2002; Hsiao 2003) refers explicitly to the inefficiency of estimating rarely changing variables in a FE approach Thomas Plümper and Vera E. Troeger
[10]
We reran all Monte Carlo experiments on rarely changing variables for different sample sizes. Specifically, we analyzed all permutations of N = {15, 30, 50, 70, 100} and T = {20, 40, 70, 100}. The results are shown in Table A2 of Appendix A. All findings for rarely changing variables remain valid for larger and smaller samples, as well as for N exceeding T and T exceeding N.
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z 3 in section 5 is rarely changing, the between and within SD for this variable are changed according to the specifications in Figs. 2–4.
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This article is about time-series–cross-sectional (TSCS) data as defined by Beck and Katz (1995) and Beck (2001). Yet, our procedure can also be applied to panels with short time series. Note that demeaning can be problematic when the number of periods is low.
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In Section 5, we assume that one z variable is rarely changing and thus almost time invariant.
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Note that the estimated coefficients of the time-varying variables remain unbiased even in the presence of correlated unit effects. However, the assumptions underlying a FE model must be satisfied (no correlated time-varying variables may exist).
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The online appendix (see the Political Analysis Web page for online appendices) demonstrates that this result also holds true when we vary the sample size. The fevd model performs best even with a comparably large T and N.
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The RE model is unbiased only when the pooled-OLS model is unbiased as well. However, the RE model is, under broad conditions, more efficient than the pooled-OLS model.
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This procedure is superficially similar to that suggested by Hsiao (2003, 52). However, Hsiao only claims that his estimate for time-invariant variables is consistent as N approaches infinity. We are interested in the small sample properties of our estimator and thus explore TSCS data. Hsiao (correctly) notes that his estimate is inconsistent for TSCS. Moreover, he neither provides SEs for his estimate nor compares his estimator to others Time-Invariant and Rarely Changing Variables
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Baltagi (2001)
[39]
We also compared the vector decomposition and the FE model to pooled-OLS and the RE model. Since all findings for time-invariant variables carry over to rarely changing variables, indicating that the vector decomposition model dominates pooled-OLS and RE models, we report the results of the RE and pooled-OLS Monte Carlos only in the online appendix.
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Details
Published
Jan 01, 2007
Vol/Issue
15(2)
Pages
124-139
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Cite This Article
Thomas Plümper, Vera E. Troeger (2007). Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects. Political Analysis, 15(2), 124-139. https://doi.org/10.1093/pan/mpm002