sphet: Spatial Models with Heteroskedastic Innovations in R
sphet is a package for estimating and testing spatial models with heteroskedastic innovations. We implement recent generalized moments estimators and semiparametric methods for the estimation of the coefficients variance-covariance matrix. This paper is a general description of sphet and all functionalities are illustrated by application to the popular Boston housing dataset. The package in its current version is limited to the estimators based on Arraiz, Drukker, Kelejian, and Prucha (2010); Kelejian and Prucha (2007, 2010). The estimation functions implemented in sphet are able to deal with virtually any sample size.
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- Conley, Timothy G. & Molinari, Francesca, 2007.
"Spatial correlation robust inference with errors in location or distance,"
Journal of Econometrics,
Elsevier, vol. 140(1), pages 76-96, September.
- Conley, Timothy G. & Molinari, Francesca, 2005. "Spatial Correlation Robust Inference with Errors in Location or Distance," Working Papers 05-12, Cornell University, Center for Analytic Economics.
- Timothy Conley & Francesca Molinari, 2005. "Spatial correlation robust inference with Errors in Location or Distance," CeMMAP working papers CWP10/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Badi H. Baltagi & Peter Egger & Michael Pfaffermayr, 2007.
"Estimating Regional Trade Agreement Effects on FDI in an Interdependent World,"
Center for Policy Research Working Papers
100, Center for Policy Research, Maxwell School, Syracuse University.
- Baltagi, Badi H. & Egger, Peter & Pfaffermayr, Michael, 2008. "Estimating regional trade agreement effects on FDI in an interdependent world," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 194-208, July.
- Achim Zeileis, . "Econometric Computing with HC and HAC Covariance Matrix Estimators," Journal of Statistical Software, American Statistical Association, vol. 11(i10).
- Pace, R. Kelley & LeSage, James P., 2004. "Chebyshev approximation of log-determinants of spatial weight matrices," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 179-196, March.
- Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.
- Luc Anselin & Nancy Lozano-Gracia, 2008. "Errors in variables and spatial effects in hedonic house price models of ambient air quality," Empirical Economics, Springer, vol. 34(1), pages 5-34, February.
- Lee, Lung-Fei, 2002. "Consistency And Efficiency Of Least Squares Estimation For Mixed Regressive, Spatial Autoregressive Models," Econometric Theory, Cambridge University Press, vol. 18(02), pages 252-277, April.
- Harrison, David Jr. & Rubinfeld, Daniel L., 1978. "Hedonic housing prices and the demand for clean air," Journal of Environmental Economics and Management, Elsevier, vol. 5(1), pages 81-102, March.
- Joris Pinkse & Margaret E. Slade & Craig Brett, 2002. "Spatial Price Competition: A Semiparametric Approach," Econometrica, Econometric Society, vol. 70(3), pages 1111-1153, May.
- Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
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