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Focused Information Criterion for Series Estimation in Partially Linear Models

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  • Naoya Sueishi
  • Arihiro Yoshimura

Abstract

This paper proposes a focused information criterion (FIC) for variable selection in partially linear models. Our criterion is designed to select an optimal model for estimating a focus parameter, which is a parameter of interest. We estimate the model by the series method and jointly select the variables in the linear part and the series length in the nonparametric part. A Monte Carlo simulation shows that the proposed FIC successfully selects the model that has a relatively small mean squared error of the estimator for the focus parameter.

Suggested Citation

  • Naoya Sueishi & Arihiro Yoshimura, 2014. "Focused Information Criterion for Series Estimation in Partially Linear Models," Discussion papers e-14-001, Graduate School of Economics Project Center, Kyoto University.
    • Handle: RePEc:kue:dpaper:e-14-001
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    References listed on IDEAS

    as
    1. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    2. Liu, Chu-An, 2015. "Distribution theory of the least squares averaging estimator," Journal of Econometrics, Elsevier, vol. 186(1), pages 142-159.
    3. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, January.
    4. Zhang, Hao Helen & Cheng, Guang & Liu, Yufeng, 2011. "Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1099-1112.
    5. Härdle, Wolfgang Karl & Huang, Li-shan, 2013. "Analysis of deviance in generalized partial linear models," SFB 649 Discussion Papers 2013-028, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Donald, S. G. & Newey, W. K., 1994. "Series Estimation of Semilinear Models," Journal of Multivariate Analysis, Elsevier, vol. 50(1), pages 30-40, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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