Recursive estimation in large panel data models: Theory and practice

التفاصيل البيبلوغرافية
العنوان: Recursive estimation in large panel data models: Theory and practice
المؤلفون: Cheng Hsiao, Jiti Gao, Bin Jiang, Yanrong Yang
المصدر: Journal of Econometrics. 224:439-465
بيانات النشر: Elsevier BV, 2021.
سنة النشر: 2021
مصطلحات موضوعية: Estimation, Economics and Econometrics, Mathematical optimization, Applied Mathematics, 05 social sciences, Population structure, Estimator, 01 natural sciences, Nonlinear programming, 010104 statistics & probability, TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES, Interactive effects, 0502 economics and business, Applied mathematics, 0101 mathematics, Recursive Bayesian estimation, 050205 econometrics, Central limit theorem, Mathematics, Panel data
الوصف: Bai (2009) proposes recursive estimation for panel data models with interactive effects. We study the behaviours of this recursive estimator. The recursive formula is established that shows the behaviours of recursive estimators depend on the initial estimator, the population structure and the iterative steps. Under some general scenarios, we find that the recursive estimator becomes consistent after the first iteration from any initials. We also obtain the optimal number of iterative steps under some prescribed conditions. The central limit theorem of the recursive estimator is established when the initial estimator is OLS. Various simulations are conducted to support our theoretical findings.
تدمد: 0304-4076
DOI: 10.1016/j.jeconom.2020.07.055
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::1af629b8090e898c0267c48c34f219bb
https://doi.org/10.1016/j.jeconom.2020.07.055
Rights: OPEN
رقم الانضمام: edsair.doi...........1af629b8090e898c0267c48c34f219bb
قاعدة البيانات: OpenAIRE
الوصف
تدمد:03044076
DOI:10.1016/j.jeconom.2020.07.055