IDEAS home Printed from https://ideas.repec.org/p/ifs/cemmap/11-18.html

Dynamic decisions under subjective expectations: a structural analysis

Author

Listed:
  • Yonghong An

    (Institute for Fiscal Studies)

  • Yingyao Hu

    (Institute for Fiscal Studies and Johns Hopkins University)

  • Ruli Xiao

    (Institute for Fiscal Studies and Indiana University)

Abstract

This paper studies dynamic discrete choices by relaxing the assumption of rational expectations. That is, agents' subjective expectations about the state transition are unknown and allowed to differ from their objectively estimable counterparts. We show that agents' subjective expectations and preferences can be identi ed and estimated from the observed conditional choice probabilities in both finite and infi nite horizon cases. Our identi cation of subjective expectations is nonparametric and can be expressed as a closed-form function of the observed conditional choice probabilities. We estimate the model primitives using maximum likelihood estimation and illustrate the good performance of estimators using Monte Carlo experiments. We apply our model to Panel Study of Income Dynamics (PSID) data and analyze women's labor participation. We find systematic differences between agents' subjective expectations about their income transition from those under rational expectations. A counterfactual analysis suggests that women with low and medium incomes would increase the probability of working under rational expectations, and that the probability would decrease for women with high income.

Suggested Citation

  • Yonghong An & Yingyao Hu & Ruli Xiao, 2018. "Dynamic decisions under subjective expectations: a structural analysis," CeMMAP working papers CWP11/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:11/18
    as

    Download full text from publisher

    File URL: https://www.ifs.org.uk/uploads/CWP111818.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Victor Aguirregabiria, 2021. "Identification of firms’ beliefs in structural models of market competition," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 54(1), pages 5-33, February.
    2. Victor Aguirregabiria & Allan Collard-Wexler & Stephen P. Ryan, 2021. "Dynamic Games in Empirical Industrial Organization," Papers 2109.01725, arXiv.org, revised Sep 2021.
    3. Victor Aguirregabiria & Jihye Jeon, 2020. "Firms’ Beliefs and Learning: Models, Identification, and Empirical Evidence," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 56(2), pages 203-235, March.
    4. Taiga Tsubota, 2021. "Identifying Dynamic Discrete Choice Models with Hyperbolic Discounting," Papers 2111.10721, arXiv.org, revised Oct 2024.
    5. Pamela Giustinelli, 2022. "Expectations in Education: Framework, Elicitation, and Evidence," Working Papers 2022-026, Human Capital and Economic Opportunity Working Group.
    6. Yingyao Hu & Yi Xin, 2019. "Identi?cation and estimation of dynamic structural models with unobserved choices," CeMMAP working papers CWP35/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. D. Nishijima & M. Oguchi, 2024. "Comparing Product Lifetime Extensions by Enhancing Consumers’ Expected Product Lifetime Among Different Durable Products," Journal of Consumer Policy, Springer, vol. 47(2), pages 223-239, June.
    8. Hu, Yingyao & Xin, Yi, 2024. "Identification and estimation of dynamic structural models with unobserved choices," Journal of Econometrics, Elsevier, vol. 242(2).
    9. Chao Wang & Stefan Weiergraeber & Ruli Xiao, 2022. "Identification of Dynamic Discrete Choice Models with Hyperbolic Discounting Using a Terminating Action," CAEPR Working Papers 2022-010 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ifs:cemmap:11/18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Emma Hyman (email available below). General contact details of provider: https://edirc.repec.org/data/cmifsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.