IDEAS home Printed from https://ideas.repec.org/p/max/cprwps/125.html
   My bibliography  Save this paper

Seemingly Unrelated Regressions with Spatial Error Components

Author

Listed:

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.

Suggested Citation

  • Badi H. Baltagi & Alain Pirotte, 2010. "Seemingly Unrelated Regressions with Spatial Error Components," Center for Policy Research Working Papers 125, Center for Policy Research, Maxwell School, Syracuse University.
    • Handle: RePEc:max:cprwps:125
    as

    Download full text from publisher

    File URL: https://surface.syr.edu/cpr/166/
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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 509-533, May.
    2. Baltagi, Badi H. & Rich, Daniel P., 2005. "Skill-biased technical change in US manufacturing: a general index approach," Journal of Econometrics, Elsevier, vol. 126(2), pages 549-570, June.
    3. 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.
    4. 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.
    5. 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.
    6. 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," Annals of Economics and Statistics, GENES, issue 87-88, pages 11-38.
    7. repec:adr:anecst:y:2007:i:87-88:p:02 is not listed on IDEAS
    8. 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.
    9. 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.
    10. Peter Egger & Michael Pfaffermayr, 2004. "Distance, trade and FDI: a Hausman-Taylor SUR approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(2), pages 227-246.
    11. 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.
    12. 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.
    13. 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 403-408, August.
    14. Avery, Robert B, 1977. "Error Components and Seemingly Unrelated Regressions," Econometrica, Econometric Society, vol. 45(1), pages 199-209, January.
    15. 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 245-260, February.
    16. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2012. "Forecasting with spatial panel data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3381-3397.
    2. Fingleton, Bernard, 2008. "Competing models of global dynamics: Evidence from panel models with spatially correlated error components," Economic Modelling, Elsevier, vol. 25(3), pages 542-558, May.
    3. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2016. "Estimation of heterogeneous panels with structural breaks," Journal of Econometrics, Elsevier, vol. 191(1), pages 176-195.
    4. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
    5. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2021. "Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 18-44, January.
    6. Pesaran, M. Hashem & Tosetti, Elisa, 2011. "Large panels with common factors and spatial correlation," Journal of Econometrics, Elsevier, vol. 161(2), pages 182-202, April.
    7. Arnab Bhattacharjee & Sean Holly, 2011. "Structural interactions in spatial panels," Empirical Economics, Springer, vol. 40(1), pages 69-94, February.
    8. Alain Pirotte & Jesús Mur, 2017. "Neglected dynamics and spatial dependence on panel data: consequences for convergence of the usual static model estimators," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(2-3), pages 202-229, July.
    9. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    10. Baltagi, Badi H. & Egger, Peter & Pfaffermayr, Michael, 2007. "Estimating models of complex FDI: Are there third-country effects?," Journal of Econometrics, Elsevier, vol. 140(1), pages 260-281, September.
    11. Fernando A. López & Román Mínguez & Jesús Mur, 2020. "ML versus IV estimates of spatial SUR models: evidence from the case of Airbnb in Madrid urban area," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 313-347, April.
    12. Huang, Yongfu, 2014. "Drivers of rising global energy demand: The importance of spatial lag and error dependence," Energy, Elsevier, vol. 76(C), pages 254-263.
    13. repec:rri:wpaper:201303 is not listed on IDEAS
    14. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2015. "Quasi Maximum Likelihood Estimation of Spatial Models with Heterogeneous Coefficients," CESifo Working Paper Series 5428, CESifo.
    15. Frank Davenport, 2017. "Estimating standard errors in spatial panel models with time varying spatial correlation," Papers in Regional Science, Wiley Blackwell, vol. 96, pages 155-177, March.
    16. Guido M. Kuersteiner & Ingmar R. Prucha, 2020. "Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity," Econometrica, Econometric Society, vol. 88(5), pages 2109-2146, September.
    17. Harald Badinger & Peter Egger, 2010. "Horizontal vs. Vertical Interdependence in Multinational Activity," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(6), pages 744-768, December.
    18. Gautam, Tej K. & Paudel, Krishna P., 2018. "The demand for natural gas in the Northeastern United States," Energy, Elsevier, vol. 158(C), pages 890-898.
    19. Georges Bresson & Cheng Hsiao, 2011. "A functional connectivity approach for modeling cross-sectional dependence with an application to the estimation of hedonic housing prices in Paris," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 501-529, December.
    20. Bernard Fingleton, 2014. "Forecasting with dynamic spatial panel data: practical implementation methods," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 194-207.
    21. Gopal K. Basak & Arnab Bhattacharjee & Samarjit Das, 2018. "Causal ordering and inference on acyclic networks," Empirical Economics, Springer, vol. 55(1), pages 213-232, August.

    More about this item

    Keywords

    Seemingly unrelated regressions; panel data; spatial dependence; heterogeneity; forecasting.;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:max:cprwps:125. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Katrina Fiacchi (email available below). General contact details of provider: https://edirc.repec.org/data/cpsyrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.