arXiv 10 May 2018 · Econometrics · publishedJournal of Business and Economic Statistics (2021) · 20 citations (OpenAlex)
arXiv:1805.04178 · PDF · DOI · OpenAlex · Extracted main text
This paper constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients as well as cross-sectional heteroskedasticity. The panel considered in this paper features a large cross-sectional dimension N but short time series T. Due to the short T, traditional methods have difficulty in disentangling the heterogeneous parameters from the shocks, which contaminates the estimates of the heterogeneous parameters. To tackle this problem, I assume that there is an underlying distribution of heterogeneous parameters, model this distribution nonparametrically allowing for correlation between heterogeneous parameters and initial conditions as well as individual-specific regressors, and then estimate this distribution by combining information from the whole panel. Theoretically, I prove that in cross-sectional homoskedastic cases, both the estimated common parameters and the estimated distribution of the heterogeneous parameters achieve posterior consistency, and that the density forecasts asymptotically converge to the oracle forecast. Methodologically, I develop a simulation-based posterior sampling algorithm specifically addressing the nonparametric density estimation of unobserved heterogeneous parameters. Monte Carlo simulations and an empirical application to young firm dynamics demonstrate improvements in density forecasts relative to alternative approaches.
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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 | –-, –- and –- (2020) Forecasting with dynamic panel data models | 0.874 | 5 | 2 | 100% |
| 2 | Pati, D., Dunson, D. B. and Tokdar, S. T (2013) Posterior consistency in conditional distribution estimation | 0.776 | 15 | 6 | 47% |
| 3 | Nguyen, X (2013) Convergence of latent mixing measures in finite and infinite mixture models | 0.737 | 3 | 3 | 67% |
| 4 | Su, Y., Bhattacharya, A., Zhang, Y., Chatterjee, N. and Carroll, R. J (2020) Nonparametric Bayesian deconvolution of a symmetric unimodal density | 0.737 | 3 | 3 | 67% |
| 5 | Akcigit, U. and Kerr, W. R (2018) Growth through heterogeneous innovations | 0.737 | 3 | 2 | 100% |
| 6 | Canale, A. and De Blasi, P (2017) Posterior asymptotics of nonparametric location-scale mixtures for multivariate density estimation | 0.669 | 10 | 4 | 30% |
| 7 | Qu, R., Timmermann, A. and Zhu, Y (2020) Comparing forecasting performance in cross-sections | 0.644 | 4 | 2 | 50% |
| 8 | –- and –- (2017) b) | 0.644 | 4 | 1 | 100% |
| 9 | –- and –- (2014) Posterior consistency in conditional density estimation by covariate dependent mixtures | 0.644 | 2 | 2 | 100% |
| 10 | Pelenis, J (2014) Bayesian regression with heteroscedastic error density and parametric mean function | 0.644 | 2 | 2 | 100% |
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