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Bayesian Estimation of Panel Models under Potentially Sparse Heterogeneity (2310.13785v2)
Published 20 Oct 2023 in econ.EM
Abstract: We incorporate a version of a spike and slab prior, comprising a pointmass at zero ("spike") and a Normal distribution around zero ("slab") into a dynamic panel data framework to model coefficient heterogeneity. In addition to homogeneity and full heterogeneity, our specification can also capture sparse heterogeneity, that is, there is a core group of units that share common parameters and a set of deviators with idiosyncratic parameters. We fit a model with unobserved components to income data from the Panel Study of Income Dynamics. We find evidence for sparse heterogeneity for balanced panels composed of individuals with long employment histories.
- Abowd, J. M., and D. Card (1989): “On the Covariance Structure of Earnings and Hours Change,” Econometrica, 57(2), 411–445.
- Atchadé, Y. F., and J. S. Rosenthal (2005): “On Adaptive Markov Chain Monte Carlo Algorithms,” Bernoulli, 11(5), 815–828.
- Baker, M. (1997): “Growth-Rate Heterogeneity and the Covariance Structure of Life-Cycle Earnings,” Journal of Labor Economics, 15(2), 338–375.
- Bonhomme, S., and E. Manresa (2015): “Grouped Patterns of Heterogeneity in Panel Data,” Econometrica, 83(3), 1147–1184.
- Browning, M., and M. Ejrnjes (2013): “Heterogeneity in the Dynamics of Labor Earning,” Annual Review of Economics, 5, 219–245.
- Browning, M., M. Ejrnjes, and J. Alvarez (2010): “Modelling Income Processes with Lots of Heterogeneity,” Review of Economic Studies, 77(4), 1353–1381.
- Castillo, I., and A. van der Vaart (2012): “Needles and Straw in a Haystack: Posterior Concentration for Possibly Sparse Sequences,” Annals of Statistics, 40(4), 2069–2101.
- Chamberlain, G., and K. Hirano (1999): “Predictive Distributions Based on Longitudinal Earnings Data,” Annales d’Economie et de Statistique, pp. 211–242.
- George, E. I., and R. E. McCulloch (1993): “Variable Selection Via Gibbs Sampling,” Journal of the American Statistical Association, 88(423), 881–889.
- (1997): “Approaches for Bayesian Variable Selection,” Statistica Sinica, 7(2), 339–373.
- Geweke, J. (1996): “Variable Selection and Model Comparison in Regression,” in Bayesian Statistics, ed. by J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, vol. 5, pp. 169–194. Oxford University Press.
- Giannone, D., M. Lenza, and G. Primiceri (2021): “Economic Predictions with Big Data: The Illusion of Sparsity,” Econometrica, 89(5), 2409–2437.
- Griffin, J. E. (2016): “An Adaptive Truncation Method for Inference in Bayesian Nonparametric Models,” Statistics and Computing, 26(1), 423–441.
- Gu, J., and R. Koenker (2017a): “Empirical Bayesball Remixed: Empirical Bayes Methods for Longitudinal Data,” Journal of Applied Economics (Forthcoming), 35(1), 781–799.
- (2017b): “Unobserved Heterogeneity in Income Dynamics: An Empirical Bayes Perspective,” Journal of Business & Economic Statistics, 35(1), 1–16.
- Guvenen, F. (2007): “Learning Your Earning: Are Labor Income Shocks Really Very Persistent?,” American Economic Review, 97(3), 687–712.
- (2009): “An Empirical Investigation of Labor Income Processes,” Review of Economic Dynamics, 12(1), 58–79.
- Haider, S. J. (2001): “Earnings Instability and Earnings Inequality of Males in the United States: 1967 - 1991,” Journal of Labor Economics, 19(4), 799–836.
- Hause, J. C. (1980): “The Fine Structure of Earnings and On-the-Job Training Hypothesis,” Econometrica, 48(4), 1013–1029.
- Hoffmann, F. (2019): “HIP, RIP, and the Robustness of Empirical Earnings Processes,” Quantitative Economics, 10, 1279–1315.
- Hospido, L. (2012): “Modelling Heterogeneity and Dynamics in the Volatility of Individual Wages,” Journal of Applied Econometrics, 27, 386–414.
- Hryshko, D. (2012): “Labor Income Profiles are Not Heterogeneous: Evidence from Income Growth Rates,” Quantitative Economics, 3, 177–209.
- Johnstone, I. M., and B. W. Silverman (2004): “Needles and Straw in Haystacks: Empirical Bayes Estimates of Possibly Sparse Sequences,” Annals of Statistics, 32(4), 1594–1649.
- Lillard, L. A., and Y. Weiss (1979): “Components of Variation in Panel Earnings Data: American Scientists 1960-70,” Econometrica, 47(2), 437–454.
- Liu, L. (2023): “Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective,” Journal of Business & Economic Statistics, forthcoming.
- Liu, L., H. R. Moon, and F. Schorfheide (2020): “Forecasting With Dynamic Panel Data Models,” Econometrica, 88(1), 171–201.
- (2023): “Forecasting With a Panel Tobit Model,” Quantitative Economics, 14(1), 117–159.
- MaCurdy, T. E. (1982): “The Use of Time Series Processes to Model the Error Structure of Earnings in a Longitudinal Data Analysis,” Journal of Econometrics, 18, 83–114.
- Meghir, C., and L. Pistaferri (2004): “Income Variance Dynamics and Heterogeneity,” Econometrica, 72(1), 1–32.
- Mitchell, T. J., and J. J. Beauchamp (1988): “Bayesian Variable Selection in Linear Regression,” Journal of the American Statistical Association, 83(404), 1023–1032.
- Nakata, T., and C. Tonetti (2015): “Small Sample Properties of Bayesian Estimators of Labor Income Processes,” Journal of Applied Economics, 18(1), 121–148.
- Rosenthal, J. S., et al. (2011): “Optimal Proposal Distributions and Adaptive MCMC,” Handbook of Markov Chain Monte Carlo, 4(10.1201), 119–138.
- Zhang, B. (2023): “Incorporating Prior Knowledge of Latent Group Structure in Panel Data,” arXiv Working Paper, 2211.16714.