arXiv 29 Jan 2020 · Econometrics
arXiv:2001.11130 · PDF · DOI · OpenAlex · Extracted main text
A recent literature in econometrics models unobserved cross-sectional heterogeneity in panel data by assigning each cross-sectional unit a one-dimensional, discrete latent type. Such models have been shown to allow estimation and inference by regression clustering methods. This paper is motivated by the finding that the clustered heterogeneity models studied in this literature can be badly misspecified, even when the panel has significant discrete cross-sectional structure. To address this issue, we generalize previous approaches to discrete unobserved heterogeneity by allowing each unit to have multiple, imperfectly-correlated latent variables that describe its response-type to different covariates. We give inference results for a k-means style estimator of our model and develop information criteria to jointly select the number clusters for each latent variable. Monte Carlo simulations confirm our theoretical results and give intuition about the finite-sample performance of estimation and model selection. We also contribute to the theory of clustering with an over-specified number of clusters and derive new convergence rates for this setting. Our results suggest that over-fitting can be severe in k-means style estimators when the number of clusters is over-specified.
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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 | Liu, R., Z. Shang, Y. Zhang, and Q. Zhou (2019) Identification and estimation in panel models with overspecified number of groups | 0.941 | 6 | 3 | 83% |
| 2 | Hansen, C. B (2007) Asymptotic properties of a robust variance matrix estimator for panel data when t is large | 0.843 | 4 | 3 | 75% |
| 3 | Bonhomme, S. and E. Manresa (2015, May) (2015) Grouped patterns of heterogeneity in panel data | 0.811 | 5 | 2 | 80% |
| 4 | Ando, T. and J. Bai (2016) Panel data models with grouped factor structure under unknown group membership | 0.737 | 3 | 2 | 100% |
| 5 | Lin, C.-C. and S. Ng (2012) Estimation of panel data models with parameter heterogeneity when group membership is unknown | 0.737 | 3 | 2 | 100% |
| 6 | Merlevede, F., M. Peligrad, and E. Rio (2011) A bernstein type inequality and moderate deviations for weakly dependent sequences | 0.511 | 2 | 2 | 50% |
| 7 | Arellano, M (1987) Computing robust standard errors for within-groups estimators | 0.405 | 1 | 1 | 100% |
| 8 | Bonhomme, S. and E. Manresa (2019) Discretizing unobserved heterogeneity | 0.405 | 1 | 1 | 100% |
| 9 | Buchinsky, Hahn, and Hotz (2005) Cluster analysis: A tool for preliminary structural analysis | 0.405 | 1 | 1 | 100% |
| 10 | Candes, E. J. and M. Soltanolkotabi (2012) A geometric analysis of subspace clustering with outliers | 0.405 | 1 | 1 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.