arXiv 30 Nov 2022 · Econometrics
arXiv:2211.16714 · PDF · DOI · OpenAlex · Extracted main text
The assumption of group heterogeneity has become popular in panel data models. We develop a constrained Bayesian grouped estimator that exploits researchers' prior beliefs on groups in a form of pairwise constraints, indicating whether a pair of units is likely to belong to a same group or different groups. We propose a prior to incorporate the pairwise constraints with varying degrees of confidence. The whole framework is built on the nonparametric Bayesian method, which implicitly specifies a distribution over the group partitions, and so the posterior analysis takes the uncertainty of the latent group structure into account. Monte Carlo experiments reveal that adding prior knowledge yields more accurate estimates of coefficient and scores predictive gains over alternative estimators. We apply our method to two empirical applications. In a first application to forecasting U.S. CPI inflation, we illustrate that prior knowledge of groups improves density forecasts when the data is not entirely informative. A second application revisits the relationship between a country's income and its democratic transition; we identify heterogeneous income effects on democracy with five distinct groups over ninety countries.
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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 | Bonhomme, S. and E. Manresa (2015) Grouped Patterns of Heterogeneity in Panel Data | 1.000 | 8 | 3 | 100% |
| 2 | Acemoglu, D., S. Johnson, J. A. Robinson, and P. Yared (2008) Income and Democracy | 0.941 | 6 | 3 | 83% |
| 3 | Paganin, S., A. H. Herring, A. F. Olshan, and D. B. Dunson (2021) Centered Partition Processes: Informative Priors for Clustering | 0.928 | 4 | 3 | 100% |
| 4 | Basu, S., A. Banerjee, and R. J. Mooney (2004) a): Active Semi-Supervision for Pairwise Constrained Clustering, in | 0.843 | 4 | 3 | 75% |
| 5 | Meila, M (2007) Comparing Clusterings—An Information Based Distance | 0.843 | 3 | 3 | 100% |
| 6 | Sethuraman, J (1994) A Constructive Definition of Dirichlet Priors | 0.830 | 7 | 3 | 57% |
| 7 | Bonhomme, S., T. Lamadon, and E. Manresa (2022) Discretizing Unobserved Heterogeneity | 0.811 | 4 | 2 | 100% |
| 8 | Ishwaran, H. and L. F. James (2001) Gibbs Sampling Methods for Stick-Breaking Priors | 0.794 | 6 | 3 | 50% |
| 9 | Walker, S. G (2007) Sampling the Dirichlet Mixture Model with Slices | 0.763 | 6 | 2 | 67% |
| 10 | Ferguson, T. S (1973) A Bayesian Analysis of Some Nonparametric Problems | 0.737 | 5 | 3 | 40% |
Showing the top 10 of 128 scored citations.