arXiv 9 Dec 2023 · Econometrics
arXiv:2312.05700 · PDF · DOI · OpenAlex · Extracted main text
The presence of units with extreme values in the dependent and/or independent variables (i.e., vertical outliers, leveraged data) has the potential to severely bias regression coefficients and/or standard errors. This is common with short panel data because the researcher cannot advocate asymptotic theory. Example include cross-country studies, cell-group analyses, and field or laboratory experimental studies, where the researcher is forced to use few cross-sectional observations repeated over time due to the structure of the data or research design. Available diagnostic tools may fail to properly detect these anomalies, because they are not designed for panel data. In this paper, we formalise statistical measures for panel data models with fixed effects to quantify the degree of leverage and outlyingness of units, and the joint and conditional influences of pairs of units. We first develop a method to visually detect anomalous units in a panel data set, and identify their type. Second, we investigate the effect of these units on LS estimates, and on other units' influence on the estimated parameters. To illustrate and validate the proposed method, we use a synthetic data set contaminated with different types of anomalous units. We also provide an empirical example.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Belotti, F. and Peracchi, F (2020) Fast leave-one-out methods for inference, model selection, and diagnostic checking | 1.000 | 6 | 4 | 100% |
| 2 | MacKinnon, J. G., Nielsen, M. ., and Webb, M. D (2023) Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust | 1.000 | 6 | 4 | 100% |
| 3 | Verardi, V. and Croux, C (2009) Robust regression in stata | 1.000 | 6 | 4 | 100% |
| 4 | Bramati, M. C. and Croux, C (2007) Robust estimators for the fixed effects panel data model | 0.965 | 10 | 5 | 90% |
| 5 | Chesher, A. and Jewitt, I (1987) The bias of a heteroskedasticity consistent covariance matrix estimator | 0.928 | 4 | 4 | 100% |
| 6 | MacKinnon, J. G. and White, H (1985) Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties | 0.928 | 4 | 4 | 100% |
| 7 | MacKinnon, J. G., Nielsen, M. ., and Webb, M. D (2023) Cluster-robust inference: A guide to empirical practice | 0.928 | 4 | 3 | 100% |
| 8 | Rousseeuw, P. J (1991) A diagnostic plot for regression outliers and leverage points | 0.928 | 4 | 3 | 100% |
| 9 | Berka, M., Devereux, M. B., and Engel, C (2018) Real exchange rates and sectoral productivity in the eurozone | 0.874 | 6 | 2 | 100% |
| 10 | Jiao, X (2022) A simple robust procedure in instrumental variables regression | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 31 scored citations.