Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
133 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Robust Analysis of Short Panels (2401.06611v1)

Published 12 Jan 2024 in econ.EM

Abstract: Many structural econometric models include latent variables on whose probability distributions one may wish to place minimal restrictions. Leading examples in panel data models are individual-specific variables sometimes treated as "fixed effects" and, in dynamic models, initial conditions. This paper presents a generally applicable method for characterizing sharp identified sets when models place no restrictions on the probability distribution of certain latent variables and no restrictions on their covariation with other variables. In our analysis latent variables on which restrictions are undesirable are removed, leading to econometric analysis robust to misspecification of restrictions on their distributions which are commonplace in the applied panel data literature. Endogenous explanatory variables are easily accommodated. Examples of application to some static and dynamic binary, ordered and multiple discrete choice and censored panel data models are presented.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (48)
  1. Andersen, E. B. (1970): “Asymptotic properties of conditional maximum-likelihood estimators,” Journal of the Royal Statistical Society Series B: Statistical Methodology, 32(2), 283–301.
  2. Aristodemou, E. (2021): “Semiparametric identification in panel data discrete response models,” Journal of Econometrics, 220(2), 253–271.
  3. Artstein, Z. (1983): “Distributions of random sets and random selections,” Israel Journal of Mathematics, 46, 313–324.
  4. Beresteanu, A., I. Molchanov, and F. Molinari (2011): “Sharp Identification Regions in Models with Convex Moment Predictions,” Econometrica, 79(6), 1785–1821.
  5. Bonhomme, S. (2012): “Functional Differencing,” Econometrica, 80(4), 1337–1385.
  6. Bonhomme, S., K. Dano, and B. Graham (2023): “Identification in a Binary Choice Panel Data Model with a Predetermined Covariate,” arXiv preprint arXiv:2301.05733.
  7. Chamberlain, G. (2010): “Binary response models for panel data: Identification and information,” Econometrica, 78(1), 159–168.
  8. Chernozhukov, V., I. Fernandez-Val, J. Hahn, and W. Newey (2013): “Average and Quantile Effects in Nonseparable Panel Models,” Econometrica, 81(2), 535–580.
  9. Chesher, A., D. Kim, and A. M. Rosen (2023): “IV Methods for Tobit Models,” Journal of Econometrics, 235(2), 1700–1724.
  10. Chesher, A., and A. M. Rosen (2017): “Generalized instrumental variable models,” Econometrica, 85(3), 959–989.
  11.    (2020): “Generalized instrumental variable models, methods, and applications,” in Handbook of Econometrics, vol. 7, pp. 1–110. Elsevier.
  12. Chesher, A., A. M. Rosen, and Z. Siddique (2023): “Estimating Endogenous Effects on Ordered Outcomes,” Review of Economics and Statistics, forthcoming.
  13. Chesher, A., A. M. Rosen, and K. Smolinski (2013): “An Instrumental Variable Model of Multiple Discrete Choice,” Quantitative Economics, 4(2), 157–196.
  14. Chesher, A., and K. Smolinski (2012): “IV Models of Ordered Choice,” Journal of Econometrics, 166, 33–48.
  15. Dano, K. (2023): “Transition Probabilities and Moment Restrictions in Dynamic Fixed Effects Logit Models,” arXiv preprint arXiv:2303.00083.
  16. Davezies, L., X. D’Haultfœuille, and L. Laage (2022): “Identification and Estimation of Average Marginal Effects in Fixed Effects Logit Models,” arXiv preprint arXiv:2105.00879.
  17. Davezies, L., X. D’Haultfœuille, and M. Mugnier (2023): “Fixed-effects binary choide models wtih three or more periods,” Quantitative Economics, 14(3), 1105–1132.
  18. Dobronyi, C. R., J. Gu, and K. i. Kim (2021): “Identification of Dynamic Panel Logit Models with Fixed Effects,” arXiv preprint arXiv:2104.04590.
  19. Dobronyi, C. R., F. Ouyang, and T. T. Yang (2023): “Revisiting Panel Data Discrete Choice Models with Lagged Dependent Variables,” arXiv preprint arXiv:2301.09379.
  20. Gao, W. Y., and M. Li (2020): “Robust Semiparametric Estimation in Panel Multinomial Choice Models,” arXiv preprint arXiv:2009.00085.
  21. Honoré, B. (1993): “Orthogonality Conditions for Tobit Models with Fixed Effects and Lagged Dependent Variables,” Journal of Econometrics, 59(1–2), 35–61.
  22. Honoré, B. E., and Á. De Paula (2021): “Identification in simple binary outcome panel data models,” The Econometrics Journal, 24(2), C78–C93.
  23. Honoré, B. E., and L. Hu (2002): “Estimation of Cross Sectional and Panel Data Censored Regression Models with Endogeneity,” Journal of Econometrics, 122(2), 293–316.
  24. Honoré, B. E., and E. Kyriazidou (1992): “Trimmed LAD and Least Squares Estimation of Truncated and Censored Regression Models with Fixed Effects,” Econometrica, 60(3), 533–565.
  25.    (2000): “Panel data discrete choice models with lagged dependent variables,” Econometrica, 68(4), 839–874.
  26.    (2019): “Panel vector autoregressions with binary data,” in Panel data econometrics, pp. 197–223. Elsevier.
  27. Honoré, B. E., C. Muris, and M. Weidner (2023): “Dynamic ordered panel logit models,” arXiv preprint arXiv:2107.03253.
  28. Honoré, B. E., and E. Tamer (2006): “Bounds on parameters in panel dynamic discrete choice models,” Econometrica, 74(3), 611–629.
  29. Honoré, B. E., and M. Weidner (2022): “Moment conditions for dynamic panel logit models with fixed effects,” arXiv preprint arXiv:2005.05942.
  30. Hu, L. (2002): “Estimation of a Censored Dynamic Panel Data Model,” Econometrica, 70(2), 2499–2517.
  31. Khan, S., F. Ouyang, and E. Tamer (2021): “Inference on semiparametric multinomial response models,” Quantitative Economics, 12(3), 743–777.
  32. Khan, S., M. Ponomareva, and E. Tamer (2016): “Identification of panel data models with endogenous censoring,” Journal of Econometrics, 194(1), 57–75.
  33. Khan, S., M. Ponomareva, and E. Tamer (2023): “Identification of dynamic binary response models,” Journal of Econometrics, 237(1), 105515.
  34. Kitazawa, Y. (2022): “Transformations and Moment Conditions for Dynamic Fixed Effects Logit Models,” Journal of Econometrics, 229(2), 350–362.
  35. Lancaster, T. (2000): “The Incidental Parameter Problem Since 1948,” Journal of Econometrics, 95(2), 391–413.
  36. Luo, Y., and H. Wang (2018): “Core Determining Class: Construction, Approximation, and Inference,” Available at SSRN: https://ssrn.com/abstract=3154285.
  37. Manski, C. F. (1987): “Semiparametric analysis of random effects linear models from binary panel data,” Econometrica, 55(2), 357–362.
  38. Mbakop, E. (2023): “Identification in Some Discrete Choice Models: A Computational Approach,” arXiv preprint arXiv:2305.15691.
  39. Molinari, F. (2020): “Microeconometrics with Partial Identification,” in The Handbook of Econometrics, ed. by S. Durlauf, L. P. Hansen, J. J. Heckman, and R. Matzkin, vol. 7a, pp. 355–486. Elsevier.
  40. Neyman, J., and E. L. Scott (1948): “Consistent Estimates Based on Partially Consistent Observations,” Econometrica, 16(1), 1–32.
  41. Pakes, A., and J. Porter (2022): “Moment inequalities for multinomial choice with fixed effects,” Quantitative Economics, forthcoming.
  42. Pakes, A., J. R. Porter, M. Shepard, and S. Calder-Wang (2021): “Unobserved heterogeneity, state dependence, and health plan choices,” Discussion paper, National Bureau of Economic Research.
  43. Ponomarev, K. (2022): “Selecting Inequalities for Sharp Identification in Models with Set-Valued Predictions,” Available at https://escholarship.org/uc/item/1kz2n299#page=80.
  44. Rasch, G. (1960): Studies in Mathematical Psychology: 1. Probabilistic models for some intelligence and attainment tests. Nielsen and Lydiche.
  45.    (1961): “On general laws and the meaning of measurement in psychology,” in Proceedings of the fourth Berkeley symposium on mathematical statistics and probability, vol. 4, pp. 321–333.
  46. Shi, X., M. Shum, and W. Song (2018): “Estimating semi-parametric panel multinomial choice models using cyclic monotonicity,” Econometrica, 86(2), 737–761.
  47. Tamer, E. (2003): “Incomplete simultaneous discrete response model with multiple equilibria,” The Review of Economic Studies, 70(1), 147–165.
  48. Wolfram Research, Inc. (2023): “Mathematica, Version 13.3,” Champaign, IL, 2023.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now