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

Alexander Mayer, Michael Massmann

arXiv 16 Sep 2023 · Econometrics · publishedJournal of Business and Economic Statistics (2025)

arXiv:2309.08982 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

90
references
161
in-text mentions
90
distinct cited
3
self-citations
12,829
main-text words

appendix boundary found by appendix_command · 47% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Hansen, B. E (1996) a): Inference when a nuisance parameter is not identified under the null hypothesis1.00053100%
2Gwak, B (2022) State-dependent formation of inflation expectations0.9209478%
3Malmendier, U. and S. Nagel (2016) Learning from inflation experiences0.90912575%
4Newey, W. K. and D. McFadden (1994) Large sample estimation and hypothesis testing, in0.9098475%
5Madeira, C. and B. Zafar (2015) Heterogeneous inflation expectations and learning0.9098375%
6Nagel, S (2024) Leaning against inflation experiences0.8746467%
7Hansen, B. E (2017) Regression kink with an unknown threshold0.8435460%
8Christopeit, N. and M. Massmann (2018) Estimating structural parameters in regression models with adaptive learning0.73732100%
9Mayer, A (2022) Estimation and inference in adaptive learning models with slowly decreasing gains self0.73732100%
10Acedański, J (2017) Heterogeneous expectations and the distribution of wealth0.64422100%

Showing the top 10 of 90 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Estimation and inference in models with multiple behavioural equilibria1.00053