Generalized spatial autocorrelation in a panel-probit model with an application to exporting in China

Badi H Baltagi, Peter H. Egger, Michaela Kesina

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

This paper proposes a generalized spatial panel-data probit model with spatial autocorrelation of the dependent variable, the time-invariant individual shocks, and the remainder disturbances. It proposes its estimation with a Bayesian Markov chain Monte Carlo procedure. Simulation results show that the proposed estimation method performs well in small- to medium-sized samples. This method is then applied to the analysis of export-market participation of 1451 Chinese firms between 2002 and 2006 in the prefecture-level city of Wenzhou in the province of Zhejiang. Empirical results show that two of the three forms of the hypothesized spatial autocorrelation are significant, namely the spatial lag for the dependent variable and the time-invariant firm-specific shocks, but not the time-variant shocks. Ignoring any of these significant spatial effects would lead to misspecification.

Original languageEnglish (US)
Pages (from-to)1-19
Number of pages19
JournalEmpirical Economics
DOIs
StateAccepted/In press - Jan 3 2018

Keywords

  • Chinese firms
  • Firm-level exporting
  • Panel-data econometrics
  • Panel-probit
  • Spatial econometrics
  • Spillovers

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics (miscellaneous)
  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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