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
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Englisch
- Erschienen in
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Series: Quaderni - Working Paper DSE ; No. 1052
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
- DOI
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doi:10.6092/unibo/amsacta/4501
- Handle
- Letzte Aktualisierung
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20.09.2024, 08:20 MESZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Arduini, Tiziano
- Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE)
Entstanden
- 2016