Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models

J. Lei

Research output: Working paperDiscussion paperOther research output

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Abstract

Abstract: This paper considers spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects, where the latent dependent variables are spatially correlated. Without imposing any parametric structure of the error terms, this paper proposes a smoothed spatial maximum score (SSMS) estimator which consistently estimates the model parameters up to scale. The identification of parameters is obtained, when the disturbances are time-stationary and the explanatory variables vary enough over time along with an exogenous and time-invariant spatial weight matrix. Consistency and asymptotic distribution of the proposed estimator are also derived in the paper. Finally, a Monte Carlo study indicates that the SSMS estimator performs quite well in finite samples.
Original languageEnglish
Place of PublicationTilburg
PublisherEconometrics
Number of pages37
Volume2013-061
Publication statusPublished - 2013

Publication series

NameCentER Discussion Paper
Volume2013-061

Fingerprint

Panel model
Binary choice
Estimator
Monte Carlo study
Asymptotic distribution
Finite sample
Binary choice model
Fixed effects
Panel data

Keywords

  • Spatial Autoregressive Models
  • Binary Choice
  • Fixed Effects
  • Maximum Score Estimation

Cite this

Lei, J. (2013). Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models. (CentER Discussion Paper; Vol. 2013-061). Tilburg: Econometrics.
Lei, J. / Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models. Tilburg : Econometrics, 2013. (CentER Discussion Paper).
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abstract = "Abstract: This paper considers spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects, where the latent dependent variables are spatially correlated. Without imposing any parametric structure of the error terms, this paper proposes a smoothed spatial maximum score (SSMS) estimator which consistently estimates the model parameters up to scale. The identification of parameters is obtained, when the disturbances are time-stationary and the explanatory variables vary enough over time along with an exogenous and time-invariant spatial weight matrix. Consistency and asymptotic distribution of the proposed estimator are also derived in the paper. Finally, a Monte Carlo study indicates that the SSMS estimator performs quite well in finite samples.",
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Lei, J 2013 'Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models' CentER Discussion Paper, vol. 2013-061, Econometrics, Tilburg.

Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models. / Lei, J.

Tilburg : Econometrics, 2013. (CentER Discussion Paper; Vol. 2013-061).

Research output: Working paperDiscussion paperOther research output

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N2 - Abstract: This paper considers spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects, where the latent dependent variables are spatially correlated. Without imposing any parametric structure of the error terms, this paper proposes a smoothed spatial maximum score (SSMS) estimator which consistently estimates the model parameters up to scale. The identification of parameters is obtained, when the disturbances are time-stationary and the explanatory variables vary enough over time along with an exogenous and time-invariant spatial weight matrix. Consistency and asymptotic distribution of the proposed estimator are also derived in the paper. Finally, a Monte Carlo study indicates that the SSMS estimator performs quite well in finite samples.

AB - Abstract: This paper considers spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects, where the latent dependent variables are spatially correlated. Without imposing any parametric structure of the error terms, this paper proposes a smoothed spatial maximum score (SSMS) estimator which consistently estimates the model parameters up to scale. The identification of parameters is obtained, when the disturbances are time-stationary and the explanatory variables vary enough over time along with an exogenous and time-invariant spatial weight matrix. Consistency and asymptotic distribution of the proposed estimator are also derived in the paper. Finally, a Monte Carlo study indicates that the SSMS estimator performs quite well in finite samples.

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KW - Fixed Effects

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M3 - Discussion paper

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Lei J. Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models. Tilburg: Econometrics. 2013. (CentER Discussion Paper).