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Model selection in the presence of incidental parameters

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  • Lee, Yoonseok
  • Phillips, Peter C.B.

Abstract

This paper considers model selection in panels where incidental parameters are present. Primary interest centers on selecting a model that best approximates the underlying structure involving parameters that are common within the panel. It is well known that conventional model selection procedures are often inconsistent in panel models and this can be so even without nuisance parameters. Modifications are then needed to achieve consistency. New model selection information criteria are developed here that use either the Kullback–Leibler information criterion based on the profile likelihood or the Bayes factor based on the integrated likelihood with a bias-reducing prior. These model selection criteria impose heavier penalties than those associated with standard information criteria such as AIC and BIC. The additional penalty, which is data-dependent, properly reflects the model complexity arising from the presence of incidental parameters. A particular example is studied in detail involving lag order selection in dynamic panel models with fixed effects. The new criteria are shown to control for over/under-selection probabilities in these models and lead to consistent order selection criteria.

Suggested Citation

  • Lee, Yoonseok & Phillips, Peter C.B., 2015. "Model selection in the presence of incidental parameters," Journal of Econometrics, Elsevier, vol. 188(2), pages 474-489.
  • Handle: RePEc:eee:econom:v:188:y:2015:i:2:p:474-489
    DOI: 10.1016/j.jeconom.2015.03.012
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    References listed on IDEAS

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    1. Lee, Yoonseok, 2012. "Bias in dynamic panel models under time series misspecification," Journal of Econometrics, Elsevier, vol. 169(1), pages 54-60.
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    Cited by:

    1. Lee, Yoonseok & Mukherjee, Debasri & Ullah, Aman, 2019. "Nonparametric estimation of the marginal effect in fixed-effect panel data models," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 53-67.
    2. repec:eee:econom:v:204:y:2018:i:2:p:147-158 is not listed on IDEAS
    3. Lee, Yoon-Jin & Okui, Ryo & Shintani, Mototsugu, 2018. "Asymptotic inference for dynamic panel estimators of infinite order autoregressive processes," Journal of Econometrics, Elsevier, vol. 204(2), pages 147-158.
    4. Majid M. Al-Sadoon & Tong Li & M. Hashem Pesaran, 2017. "Exponential class of dynamic binary choice panel data models with fixed effects," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 898-927, October.
    5. Lee, Yoonsoo & Mukoyama, Toshihiko, 2015. "Productivity and employment dynamics of US manufacturing plants," Economics Letters, Elsevier, vol. 136(C), pages 190-193.
    6. Greenaway-McGrevy, Ryan & Hood, Kyle K., 2016. "Worker migration or job creation? Persistent shocks and regional recoveries," Journal of Urban Economics, Elsevier, vol. 96(C), pages 1-16.
    7. Haruo Iwakura & Ryo Okui, 2014. "Asymptotic Efficiency in Factor Models and Dynamic Panel Data Models," KIER Working Papers 887, Kyoto University, Institute of Economic Research.
    8. Ruiqi Liu & Anton Schick & Zuofeng Shang & Yonghui Zhang & Qiankun Zhou, 2018. "Identification and estimation in panel models with overspecified number of groups," Departmental Working Papers 2018-03, Department of Economics, Louisiana State University.

    More about this item

    Keywords

    (Adaptive) model selection; Incidental parameters; Profile likelihood; Kullback–Leibler information; Integrated likelihood; Bias-reducing prior; Fixed effects; Lag order;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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