arXiv 5 Jul 2020 · Econometrics · 1 citations (OpenAlex)
arXiv:2007.02435 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we estimate and leverage latent constant group structure to generate the point, set, and density forecasts for short dynamic panel data. We implement a nonparametric Bayesian approach to simultaneously identify coefficients and group membership in the random effects which are heterogeneous across groups but fixed within a group. This method allows us to flexibly incorporate subjective prior knowledge on the group structure that potentially improves the predictive accuracy. In Monte Carlo experiments, we demonstrate that our Bayesian grouped random effects (BGRE) estimators produce accurate estimates and score predictive gains over standard panel data estimators. With a data-driven group structure, the BGRE estimators exhibit comparable accuracy of clustering with the Kmeans algorithm and outperform a two-step Bayesian grouped estimator whose group structure relies on Kmeans. In the empirical analysis, we apply our method to forecast the investment rate across a broad range of firms and illustrate that the estimated latent group structure improves forecasts relative to standard panel data estimators.
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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 | Ishwaran, H. and L. F. James (2001) Gibbs sampling methods for stick-breaking priors | 0.941 | 6 | 3 | 83% |
| 2 | Escobar, M. D. and M. West (1995) Bayesian density estimation and inference using mixtures | 0.843 | 4 | 3 | 75% |
| 3 | Liu, L (2020) Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective | 0.811 | 5 | 2 | 80% |
| 4 | Bonhomme, S. and E. Manresa (2015) Grouped patterns of heterogeneity in panel data | 0.811 | 4 | 2 | 100% |
| 5 | Walker, S. G (2007) Sampling the Dirichlet mixture model with slices | 0.794 | 6 | 3 | 50% |
| 6 | Gala, V. D., J. F. Gomes, and T. Liu (2019) Investment without q | 0.585 | 3 | 1 | 100% |
| 7 | Hastie, D. I., S. Liverani, and S. Richardson (2015) Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations | 0.511 | 3 | 2 | 33% |
| 8 | Bonhomme, S., T. Lamadon, and E. Manresa (2019) Discretizing unobserved heterogeneity | 0.511 | 2 | 1 | 100% |
| 9 | Kim, J. and L. Wang (2019) Hidden group patterns in democracy developments: Bayesian inference for grouped heterogeneity | 0.511 | 2 | 1 | 100% |
| 10 | Ando, T. and J. Bai (2016) Panel data models with grouped factor structure under unknown group membership | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models | 0.405 | 1 | 1 |