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Sieve maximum likelihood estimation of the spatial autoregressive Tobit model

Xingbai Xu and Lung-Fei Lee ()

Journal of Econometrics, 2018, vol. 203, issue 1, 96-112

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.

Keywords: Spatial autoregressive model; Tobit model; Sieve maximum likelihood estimation; Near-epoch dependence; Social network (search for similar items in EconPapers)
JEL-codes: C14 C21 C24 C63 (search for similar items in EconPapers)
Date: 2018
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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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Handle: RePEc:eee:econom:v:203:y:2018:i:1:p:96-112