Estimating Nonlinearities in Spatial Autoregressive Models
AbstractIn spatial autoregressive models, the functional form of autocorrelation is assumed to be linear. In this paper, we propose a simple semiparametric procedure, based on Yatchew's (1998) partial linear least squares, that relaxes this restriction. Simple simulations show that this model outperforms traditional SAR estimation when nonlinearities are present. We then apply the methodology on real data to test for the spatial pattern of voting for independent candidates in US presidential elections. We ﬁnd that in some counties, votes for “third candidates” are non-linearly related to votes for “third candidates” in neighboring counties, which pleads for strategic behavior.
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Date of creation: 13 Jan 2010
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Spatial econometrics; semiparametric estimations;
Other versions of this item:
- Nicolas Debarsy & Vincenzo Verardi, 2010. "Estimating Nonlinearities in Spatial Autoregressive Models," Working Papers 1016, University of Namur, Department of Economics.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-01-30 (All new papers)
- NEP-ECM-2010-01-30 (Econometrics)
- NEP-ETS-2010-01-30 (Econometric Time Series)
- NEP-GEO-2010-01-30 (Economic Geography)
- NEP-URE-2010-01-30 (Urban & Real Estate Economics)
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