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Least squares estimation in nonstationary nonlinear cohort panels with learning from experience

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  • Alexander Mayer
  • Michael Massmann

Abstract

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.

Suggested Citation

  • Alexander Mayer & Michael Massmann, 2023. "Least squares estimation in nonstationary nonlinear cohort panels with learning from experience," Papers 2309.08982, arXiv.org, revised Jan 2025.
    • Handle: RePEc:arx:papers:2309.08982
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    References listed on IDEAS

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    3. Alexander Mayer, 2022. "Estimation and inference in adaptive learning models with slowly decreasing gains," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(5), pages 720-749, September.
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    5. Ulrike Malmendier, 2021. "FBBVA Lecture 2020: Exposure, Experience, and Expertise: Why Personal Histories Matter in Economics [Macroeconomic Experiences and Risk Taking of Euro Area Households]," Journal of the European Economic Association, European Economic Association, vol. 19(6), pages 2857-2894.
    6. Park, Joon Y & Phillips, Peter C B, 2001. "Nonlinear Regressions with Integrated Time Series," Econometrica, Econometric Society, vol. 69(1), pages 117-161, January.
    7. Ulrike Malmendier, 2021. "Exposure, Experience, and Expertise: Why Personal Histories Matter in Economics," NBER Working Papers 29336, National Bureau of Economic Research, Inc.
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