Arbeitspapier

Distribution Free Estimation of Spatial Autoregressive Binary Choice Panel Data Models

This paper proposes a semiparametric estimator for spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects. The estimation procedure is based on the observational equivalence between distribution free models with a conditional median restriction and parametric models (such as Logit/Probit) exhibiting (multiplicative) heteroskedasticity and autocorrelation. Without imposing any parametric structure on the error terms, we consider the semiparametric nonlinear least squares (NLLS) estimator for this model and analyze its asymptotic properties under spatial near-epoch dependence. The main advantage of our method over the existing estimators is that it consistently estimates choice probabilities. The finite-dimensional estimator is shown to be consistent and root-n asymptotically normal under some reasonable conditions. Finally, a Monte Carlo study indicates that the estimator performs quite well in finite samples.

Sprache
Englisch

Erschienen in
Series: Quaderni - Working Paper DSE ; No. 1052

Klassifikation
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
General Regional Economics: Econometric and Input-Output Models; Other Models

Ereignis
Geistige Schöpfung
(wer)
Arduini, Tiziano
Ereignis
Veröffentlichung
(wer)
Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE)
(wo)
Bologna
(wann)
2016

DOI
doi:10.6092/unibo/amsacta/4501
Handle
Letzte Aktualisierung
20.09.2024, 08:20 MESZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Arduini, Tiziano
  • Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE)

Entstanden

  • 2016

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