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Sieve maximum likelihood estimation of the spatial autoregressive Tobit model

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  • Xu, Xingbai
  • Lee, Lung-fei

Abstract

This paper extends the ML estimation of a spatial autoregressive Tobit model under normal disturbances in Xu and Lee (2015b, Journal of Econometrics) to distribution-free estimation. We examine the sieve MLE of the model, where the disturbances are i.i.d.with an unknown distribution. We show that the spatial autoregressive process with Tobit censoring and related variables are spatial near-epoch dependent (NED). A related contribution is that we develop some exponential inequalities for spatial NED random fields. With these inequalities, we establish the consistency of the estimator. Asymptotic distributions of structural parameters of the model are derived from a functional central limit theorem and projection.

Suggested Citation

  • Xu, Xingbai & Lee, Lung-fei, 2018. "Sieve maximum likelihood estimation of the spatial autoregressive Tobit model," Journal of Econometrics, Elsevier, vol. 203(1), pages 96-112.
    • Handle: RePEc:eee:econom:v:203:y:2018:i:1:p:96-112
    DOI: 10.1016/j.jeconom.2017.10.008
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    More about this item

    Keywords

    Spatial autoregressive model; Tobit model; Sieve maximum likelihood estimation; Near-epoch dependence; Social network;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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