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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 Mar 2024.
    • Handle: RePEc:arx:papers:2309.08982
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    References listed on IDEAS

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    1. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
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    3. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    4. George W. Evans, 2001. "Expectations in Macroeconomics. Adaptive versus Eductive Learning," Revue Économique, Programme National Persée, vol. 52(3), pages 573-582.
    5. Ulrike Malmendier, 2021. "Exposure, Experience, and Expertise: Why Personal Histories Matter in Economics," NBER Working Papers 29336, National Bureau of Economic Research, Inc.
    6. 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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