Integrated likelihood based inference for nonlinear panel data models with unobserved effects
| Autor: | Thomas A. Severini, Martin Schumann, Gautam Tripathi |
|---|---|
| Přispěvatelé: | QE Econometrics, RS: GSBE other - not theme-related research |
| Rok vydání: | 2021 |
| Předmět: |
Single Equation Models
Single Variables: Models with Panel Data Longitudinal Data Spatial Time Series Economics and Econometrics media_common.quotation_subject BIAS REDUCTION Inference INCIDENTAL PARAMETER PROBLEM 01 natural sciences 010104 statistics & probability 0502 economics and business Econometrics Economics 0101 mathematics Statistical theory Panel data 050205 econometrics media_common Fixed effects Nonlinear models Integrated likelihood Applied Mathematics 05 social sciences Estimator Fixed effects model Infinity Marginal likelihood Nonlinear system DYNAMIC-MODELS |
| Zdroj: | Journal of Econometrics, 223(1), 73-95. Elsevier Science |
| ISSN: | 0304-4076 |
| DOI: | 10.1016/j.jeconom.2020.10.001 |
| Popis: | We propose a new integrated likelihood based approach for estimating panel data models when the unobserved individual effects enter the model nonlinearly. Unlike existing integrated likelihoods in the literature, the one we propose is closer to a genuine likelihood. Although the statistical theory for the proposed estimator is developed in an asymptotic setting where the number of individuals and the number of time periods both approach infinity, results from a simulation study suggest that our methodology can work very well even in moderately sized panels of short duration in both static and dynamic models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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