Clustering of auto supplier plants in the United States: Generalized method of moments spatial logit for large samples

Thomas Klier, Daniel P. McMillen

Research output: Contribution to journalArticlepeer-review

Abstract

A linearized logit version of Pinkse and Slade's spatial GMM estimator reduces estimation to two steps - standard logit followed by two-stage least squares. Linearization produces a model that can be estimated using large datasets. Monte Carlo experiments suggest that the linearized model accurately identifies the presence of spatial effects and is capable of producing accurate estimates of marginal effects. In an application to the location of supplier plants in the U.S. auto industry, the results imply no additional clustering of new plants beyond the level of clustering of existing plant locations.

Original languageEnglish (US)
Pages (from-to)460-471
Number of pages12
JournalJournal of Business and Economic Statistics
Volume26
Issue number4
DOIs
StatePublished - Sep 2008

Keywords

  • Agglomeration
  • Automobile industry
  • Spatial GMM

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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