The Evolution of Econometric Modeling: A Guide to Influential Papers on Panel Data

Written by lossfunctiontech | Published 2025/09/10
Tech Story Tags: loss-function | econometrics | empirical-bayes | panel-data | statistical-modeling | income-dynamics | financial-modeling | bayesian-inference

TLDRExplore a curated list of influential academic references covering the history and modern developments in empirical Bayes, panel data econometrics, and income dynamics.via the TL;DR App

Table of Links

Abstract and 1. Introduction

  1. The Compound Decision Paradigm
  2. Parametric Priors
  3. Nonparametric Prior Estimation
  4. Empirical Bayes Methods for Discrete Data
  5. Empirical Bayes Methods for Panel Data
  6. Conclusion

Appendix A. Tweedie’s Formula

Appendix B. Predictive Distribution Comparison

References

References

Arellano M (2003) Panel Data Econometrics. Oxford U. Press

Arellano M, Blundell R, Bonhomme S (2017) Earnings and consumption dynamics: A nonlinear panel data framework. Econometrica 85:693–734

Azevedo EM, Deng A, Montiel Olea JL, Rao J, Weyl EG (2020) A/B testing with fat tails. Journal of Political Economy 128:4614–4672

Balestra P, Nerlove M (1966) Pooling cross section and time series data in the estimation of a dynamic model: The demand for natural gas. Econometrica 34:585–612

Beckett L, Diaconis P (1994) Spectral analysis for discrete longitudinal data. Advances in Mathematics 103:107–128

Blundell R (2014) Income dynamics and life-cycle inequality: Mechanisms and controversies. The Economic Journal 124:289–318

Browning M, Ejrnæs M, Alvarez J (2010) Modelling income proceses with lots of heterogeneity. Review of Economic Studies 77:1353–1381

Buhlmann H, Straub E (1970) Galubwurdigkeit fur schadensatze. Bulletin of Swiss Association of Actuaries pp 111–133

Castillo I, van der Vaart A (2012) Needles and straw in a haystack: Posterior concentration for possibly sparse sequences. Annals of Statistics 40:2069–2101

Chamberlain G, Hirano K (1999) Predictive distribution based on longitudinal earnings data. Annales d’ Economie et de Statistique 55/56:211–242

Chamberlain G, Leamer EE (1976) Matrix weighted averages and posterior bounds. Journal of the Royal Statistical Society Series B (Methodological) 38:73–84

Chen X, Liao Z (2014) Sieve m inference on irregular parameters. Journal of Econometrics 182:70–86

Cox DR (1975) A note on partially Bayes inference and the linear model. Biometrika 62:651–654

Deaton A (2018–) Deaton review of inequalities, https://ifs.org.uk/inequality/

Dempster A, Laird N, Rubin D (1977) Maximum likelihood from incomplete data via the em algorithm. JRSSB 39:1–38

Efron B (2010) Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambridge U. Press: Cambridge

Efron B (2014) Two Modeling Strategies for Empirical Bayes Estimation. Statistical Science 29:285 – 301

Efron B (2016) Empirical Bayes deconvolution estimates. Biometrika 103:1–20

Efron B, Morris C (1973) Combining possibly related estimation problems. Journal of the Royal Statistical Society Series B 35:379–421

Fan J, Zhang C, Zhang J (2001) Generalized likelihood ratio statistics and Wilks phenomenon. The Annals of Statistics 29:153–193

de Finetti B (1964) Foresight: Its logical laws and subjective sources. In: Kyburg HE, Smokler H (eds) Studies in Subjective Probability, Wiley, pp 93–158

Fisher RA, Corbet AS, Williams CB (1943) The relation between the number of species and the number of individuals in a random sample of an animal population.

Journal of Animal Ecology 12:42 –58

Friedman M (1957) Theory of the Consumption Function. Princeton University Press

Geweke J, Keane M (2000) An empirical analysis of earnings dynamics among men in the PSID: 1968 - 1989. Journal of Econometrics 96:293–356

Gu J, Koenker R (2017a) Empirical Bayesball remixed: Empirical Bayes methods for longitudinal data. Journal of Applied Econometrics 32:575–599

Gu J, Koenker R (2017b) Unobserved heterogeneity in income dynamics: An empirical Bayes perspective. Journal of Business and Economic Statistics 35:1–16

Gu J, Koenker R (2022) Ranking and selection from pairwise comparisons: Empirical bayes methods for citation analysis. AEA Papers and Proceedings 112:624–629

Gu J, Koenker R (2023) Invidious comparisons: Ranking and selection as compound decisions. Econometrica 91:1–41

Guvenen F, Karahan F, Ozkan S, Song J (2022) What do data on millions of u.s. workers reveal about lifecycle earnings dynamics? Econometrica 89:2303–2339

Han Q, Wellner JA (2016) Approximation and estimation of s-concave densities via R´enyi divergences. The Annals of Statistics 44:1332 – 1359

Harvey AC (1990) Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press

Heckman J, Singer B (1984) A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica 52:63–132

Hirano K (2002) Semiparametric Bayesian inference in autoregressive panel data models. Econometrica 70:781–799

Hospido L (2012) Modelling heterogeneity and dynamics in the volatility of individual wages. Journal of Applied Econometrics 27:386–411

van Houwelingen J, Stijnen T (1983) Monotone Empirical Bayes Estimators for the Continuous One-parameter Exponential Family. Statistica Neerlandica 37:29–43

Ignatiadis N, Sen B (2023) Empirical partially Bayes multiple testing and compound χ 2 decisions. Available from: https://arxiv.org/abs/2303.02887

James W, Stein C (1961) Estimation with quadratic loss. In: Proceedings of the fourth Berkeley symposium on mathematical statistics and probability, Univ of California Press, p 361

Jiang W, Zhang CH (2009) General maximum likelihood empirical Bayes estimation of normal means. Annals of Statistics 37:1647–1684

Johnstone I, Silverman B (2004) Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences. Annals of Statistics 32:1594–1649

Kiefer J, Wolfowitz J (1956) Consistency of the maximum likelihood estimator in the presence of infinitely many incidental parameters. The Annals of Mathematical Statistics 27:887–906

Kline PM, Walters CR (2021) Reasonable doubt: Experimental detection of job-level employment discrimination. Econometrica 89:765–792

Koenker R, Gu J (2024) Empirical Bayes: Some Tools, Rules and Duals. Cambridge University Press

Koenker R, Mizera I (2010) Quasi-concave density estimation. The Annals of Statistics 38(5):2998–3027

Koenker R, Mizera I (2014) Convex optimization, shape constraints, compound decisions, and empirical Bayes rules. J of American Statistical Association 109:674–685

Koenker R, Mizera I (2018) Shape Constrained Density Estimation Via Penalized R´enyi Divergence. Statistical Science 33:510 – 526

Laird N (1978) Nonparametric maximum likelihood estimation of a mixing distribution. Journal of the American Statistical Association 73:805–811

Leamer EE, Chamberlain G (1976) A Bayesian interpretation of pretesting. Journal of the Royal Statistical Society Series B (Methodological) 38:85–94

Lindley DV, Smith AF (1972) Bayes estimates for the linear model. Journal of the Royal Statistical Society Series B pp 1–41

Lindsay B (1981) Properties of the maximum likelihood estimator of a mixing distribution. In: Taillie C, Patil GP, Baldessari BA (eds) Statistical Distributions in Scientific Work, D. Reidel, pp 95–109

Lindsay B (1995) Mixture models: theory, geometry and applications. In: NSF-CBMS regional conference series in probability and statistics

Liu J (1996) Nonparametric hierarchical Bayes via sequential imputations. The Annals of Statistics 24(3):911–930

Meghir C, Pistaferri L (2004) Income variance dynamics and heterogeneity. Econometrica 72:1–32

Murphy SA, van der Vaart AW (2000) On profile likelihood. Journal of the American Statistical Association 95:449–465

Pfanzagl J (1988) Consistency of maximum likelihood estimators for certain nonparametric families, in particular: mixtures. Journal of Statistical Planning and Inference 19:137–158

Polyanskiy Y, Wu Y (2020) Self-regularizing property of nonparametric maximum likelihood estimator in mixture models, available from https://arxiv.org/abs/ 2008.08244

Polyanskiy Y, Wu Y (2021) Sharp regret bounds for empirical Bayes and compound decision problems, available from https://arxiv.org/abs/2109.03943

Robbins H (1950) A generalization of the method of maximum likelihood: Estimating a mixing distribution (abstract). The Annals of Mathematical Statistics 21:314–315

Robbins H (1951) Asymptotically subminimax solutions of compound statistical decision problems. In: Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability, University of California Press: Berkeley, vol I

Robbins H (1956) An empirical Bayes approach to statistics. In: Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, University of California Press: Berkeley, vol I

Savage LJ (1954) Foundations of Statistics. Wiley

Soloff JA, Guntuboyina A, Sen B (2021) Multivariate, heteroscedastic empirical Bayes via nonparametric maximum likelihood, available from https://arxiv.org/abs/ 2109.03466

Stein C (1956) Inadmissibility of the usual estimator of the mean of a multivariate normal distribution. In: Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, University of California Press: Berkeley, vol I, pp 197–206

Stigler SM (1990) A Galtonian Perspective on Shrinkage Estimators. Statistical Science 5:147 – 155

Teicher H (1961) Identifiability of Mixtures. The Annals of Mathematical Statistics 32:244–248

Teicher H (1967) Identifiability of mixtures of product measures. The Annals of Mathematical Statistics 38:1300–1302

Tweedie MCK (1947) Functions of a statistical variate with given means, with special reference to Laplacian distributions. Mathematical Proceedings of the Cambridge Philosophical Society 43:41–49

Wald A (1950) Statistical Decision Functions. Wiley

Zhang Y, Cui Y, Sen B, Toh KC (2022) On efficient and scalable computation of the nonparametric maximum likelihood estimator in mixture models. Available from: https://arxiv.org/abs/2208.07514

Authors:

(1) Roger Koenker;

(2) Jiaying Gu.


This paper is available on arxiv under CC BY 4.0 DEED license.


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Published by HackerNoon on 2025/09/10