Least squares estimation in nonstationary nonlinear cohort panels with learning from experience
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909448184791040 |
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| author | Mayer, Alexander Massmann, Michael |
| author_facet | Mayer, Alexander Massmann, Michael |
| contents | We discuss techniques of estimation and inference for nonstationary nonlinear cohort panels with learning from experience, showing, inter alia, the consistency and asymptotic normality of the nonlinear least squares estimator used in empirical practice. Potential pitfalls for hypothesis testing are identified and solutions proposed. Monte Carlo simulations verify the properties of the estimator and corresponding test statistics in finite samples, while an application to a panel of survey expectations demonstrates the usefulness of the theory developed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08982 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Least squares estimation in nonstationary nonlinear cohort panels with learning from experience Mayer, Alexander Massmann, Michael Econometrics Statistics Theory We discuss techniques of estimation and inference for nonstationary nonlinear cohort panels with learning from experience, showing, inter alia, the consistency and asymptotic normality of the nonlinear least squares estimator used in empirical practice. Potential pitfalls for hypothesis testing are identified and solutions proposed. Monte Carlo simulations verify the properties of the estimator and corresponding test statistics in finite samples, while an application to a panel of survey expectations demonstrates the usefulness of the theory developed. |
| title | Least squares estimation in nonstationary nonlinear cohort panels with learning from experience |
| topic | Econometrics Statistics Theory |
| url | https://arxiv.org/abs/2309.08982 |