Second-order polynomial spatial error model. Global and local spatial dependence in unemployment in Andalusia
AbstractThis paper analyses a second-order polynomial spatial structure in the residues of a regression model. We propose a new specification that captures spatial dependence on two different levels, adding a new autoregressive cycle to the errors of the classical spatial error model (SEM). The inference problems of the parameters are solved by means of maximum likelihood estimation. The model is confirmed to identify two spatial structures of spatial dependence, global and local, by an empirical application in the analysis of municipal unemployment in the Spanish region of Andalusia. Finally, Monte Carlo is implemented to evaluate the performance of this strategy in a context of finite size samples.
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Bibliographic InfoArticle provided by Elsevier in its journal Economic Modelling.
Volume (Year): 33 (2013)
Issue (Month): C ()
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Web page: http://www.elsevier.com/locate/inca/30411
Spatial econometric; Higher order spatial models; Instability spatial dependence; Local models; Unemployment; Andalusia;
Find related papers by JEL classification:
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
- E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution
- J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
- R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
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