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Seemingly Unrelated Regressions with Spatial Error Components

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Abstract

This paper considers various estimators using panel data seemingly unrelated regressions (SUR) with spatial error correlation. The true data generating process is assumed to be SUR with spatial error of the autoregressive or moving average type. Moreover, the remainder term of the spatial process is assumed to follow an error component structure. Both maximum likelihood and generalized moments (GM) methods of estimation are used. Using Monte Carlo experiments, we check the performance of these estimators and their forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous versus homogeneous panel data models.

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Bibliographic Info

Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 125.

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Length: 39 pages
Date of creation: Sep 2010
Date of revision:
Handle: RePEc:max:cprwps:125

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Keywords: Seemingly unrelated regressions; panel data; spatial dependence; heterogeneity; forecasting.;

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References

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  1. Srivastava, V. K. & Dwivedi, T. D., 1979. "Estimation of seemingly unrelated regression equations : A brief survey," Journal of Econometrics, Elsevier, vol. 10(1), pages 15-32, April.
  2. Wan, Guang H & Griffiths, William E & Anderson, Jock R, 1992. "Using Panel Data to Estimate Risk Effects in Seemingly Unrelated Production Functions," Empirical Economics, Springer, vol. 17(1), pages 35-49.
  3. Beierlein, James G & Dunn, James W & McConnon, James C, Jr, 1981. "The Demand for Electricity and Natural Gas in the Northeastern United States," The Review of Economics and Statistics, MIT Press, vol. 63(3), pages 403-08, August.
  4. Kinal, Terrence & Lahiri, Kajal, 1990. "A computational algorithm for multiple equation models with panel data," Economics Letters, Elsevier, vol. 34(2), pages 143-146, October.
  5. Baltagi, Badi H & Griffin, James M & Rich, Daniel P, 1995. "Airline Deregulation: The Cost Pieces of the Puzzle," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 36(1), pages 245-60, February.
  6. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-33, May.
  7. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
  8. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
  9. Peter Egger & Michael Pfaffermayr, 2001. "Distance, Trade and FDI: A Hausman-Taylor SUR Approach," WIFO Working Papers 164, WIFO.
  10. Howrey, E. Philip & Varian, Hal R., 1984. "Estimating the distributional impact of time-of-day pricing of electricity," Journal of Econometrics, Elsevier, vol. 26(1-2), pages 65-82.
  11. Badi H. BALTAGI & Peter EGGER & Michael PFAFFERMAYR, 2007. "A Monte Carlo Study for Pure and Pretest Estimators of a Panel Data Model with Spatially Autocorrelated Disturbances," Annales d'Economie et de Statistique, ENSAE, issue 87-88, pages 11-38.
  12. Brown, Philip & Kleidon, Allan W. & Marsh, Terry A., 1983. "New evidence on the nature of size-related anomalies in stock prices," Journal of Financial Economics, Elsevier, vol. 12(1), pages 33-56, June.
  13. Baltagi, Badi H. & Rich, Daniel P., 2003. "Skill-Biased Technical Change in U.S. Manufacturing: A General Index Approach," IZA Discussion Papers 841, Institute for the Study of Labor (IZA).
  14. Sickles, Robin C., 1985. "A nonlinear multivariate error components analysis of technology and specific factor productivity growth with an application to the U.S. Airlines," Journal of Econometrics, Elsevier, vol. 27(1), pages 61-78, January.
  15. Avery, Robert B, 1977. "Error Components and Seemingly Unrelated Regressions," Econometrica, Econometric Society, vol. 45(1), pages 199-209, January.
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Cited by:
  1. W.E. Griffiths & Ma. Rebecca Valenzuela, 2004. "Gibbs Samplers for a Set of Seemingly Unrelated Regressions," Department of Economics - Working Papers Series 912, The University of Melbourne.
  2. Chakir, Raja & Le Gallo, Julie, 2013. "Predicting land use allocation in France: A spatial panel data analysis," Ecological Economics, Elsevier, vol. 92(C), pages 114-125.
  3. Baylis, Katherine R. & Paulson, Nicholas D. & Piras, Gianfranco, 2011. "Spatial Approaches to Panel Data in Agricultural Economics: A Climate Change Application," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(03), August.
  4. Alexander Behar, 2011. "Price Discovery and Price Risk Management Before and After Deregulation of the South African Maize Industry," Working Papers 263, Economic Research Southern Africa.
  5. Karolina Lewandowska-Gwarda, 2013. "Migracje zagraniczne w Polsce - analiza z wykorzystaniem przestrzennego modelu SUR," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 30, pages 43-57.
  6. Hauptmeier, Sebastian & Mittermaier, Ferdinand & Rincke, Johannes, 2012. "Fiscal competition over taxes and public inputs," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 407-419.

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