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On the Finite Sample Properties of Pre-test Estimators of Spatial Models

Author

Listed:
  • Gianfranco Piras

    () (Regional Research Institute, West Virginia University)

  • Ingmar R. Prucha

    () (Department of Economics, University of Maryland)

Abstract

This paper explores the properties of pre-tst strategies in estimating a linear Cliff-Ord -type spatial model when the researcher is unsure about the nature of the spatial dependence. More specifically, the paper explores the finite sample properties of the pre-test estimators introduced in Florax et al. (2003), which are based on Lagrange Multiplier (LM) tests, within the context of a Monte Carlo study. The performance of those estimators is compared with that of the maximum likelihood (ML) estimator of the encompassing model. We find that, even in a very simple setting, the bias of the estimates generated by pre-testing strategies can be very large in some cases and the empirical size of tests can differ substantially from the nominal size. This is in contrast to the ML estimator.

Suggested Citation

  • Gianfranco Piras & Ingmar R. Prucha, 2013. "On the Finite Sample Properties of Pre-test Estimators of Spatial Models," Working Papers Working Paper 2013-07, Regional Research Institute, West Virginia University.
  • Handle: RePEc:rri:wpaper:2013wp07
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    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. Debarsy, Nicolas & Ertur, Cem, 2010. "Testing for spatial autocorrelation in a fixed effects panel data model," Regional Science and Urban Economics, Elsevier, vol. 40(6), pages 453-470, November.
    3. Florax, Raymond & Folmer, Henk, 1992. "Specification and estimation of spatial linear regression models : Monte Carlo evaluation of pre-test estimators," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 405-432, September.
    4. Florax, Raymond J. G. M. & Folmer, Hendrik & Rey, Sergio J., 2003. "Specification searches in spatial econometrics: the relevance of Hendry's methodology," Regional Science and Urban Economics, Elsevier, vol. 33(5), pages 557-579, September.
    5. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    6. Irani Arraiz & David M. Drukker & Harry H. Kelejian & Ingmar R. Prucha, 2010. "A Spatial Cliff‐Ord‐Type Model With Heteroskedastic Innovations: Small And Large Sample Results," Journal of Regional Science, Wiley Blackwell, vol. 50(2), pages 592-614, May.
    7. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    8. Baltagi, Badi H. & Liu, Long, 2008. "Testing for random effects and spatial lag dependence in panel data models," Statistics & Probability Letters, Elsevier, vol. 78(18), pages 3304-3306, December.
    9. Leeb, Hannes & P tscher, Benedikt M., 2008. "Guest Editors' Editorial: Recent Developments In Model Selection And Related Areas," Econometric Theory, Cambridge University Press, vol. 24(02), pages 319-322, April.
    10. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    11. Baltagi, Badi H. & Heun Song, Seuck & Cheol Jung, Byoung & Koh, Won, 2007. "Testing for serial correlation, spatial autocorrelation and random effects using panel data," Journal of Econometrics, Elsevier, vol. 140(1), pages 5-51, September.
    12. Bera, Anil K. & Yoon, Mann J., 1993. "Specification Testing with Locally Misspecified Alternatives," Econometric Theory, Cambridge University Press, vol. 9(4), pages 649-658, August.
    13. Badi H. Baltagi & Peter Egger & Michael Pfaffermayr, 2013. "A Generalized Spatial Panel Data Model with Random Effects," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 650-685, August.
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    Citations

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    Cited by:

    1. Nagayasu, Jun, 2014. "Regional inflation, spatial location and the Balassa-Samuelson effect," MPRA Paper 59220, University Library of Munich, Germany.
    2. Harry H. Kelejian, 2016. "Critical issues in spatial models: error term specifications, additional endogenous variables, pre-testing, and Bayesian analysis," Letters in Spatial and Resource Sciences, Springer, vol. 9(1), pages 113-136, March.
    3. Prodosh Simlai, 2018. "Spatial Dependence, Idiosyncratic Risk, and the Valuation of Disaggregated Housing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 57(2), pages 192-230, August.
    4. Pede, Valerien O. & Florax, Raymond J.G.M. & Lambert, Dayton M., 2014. "Spatial econometric STAR models: Lagrange multiplier tests, Monte Carlo simulations and an empirical application," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 118-128.
    5. Jun Nagayasu, 2017. "Regional inflation, spatial locations and the Balassa-Samuelson effect: Evidence from Japan," Urban Studies, Urban Studies Journal Limited, vol. 54(6), pages 1482-1499, May.

    More about this item

    Keywords

    cliff-ord; spatial; model; lagrange multiplier; monte carlo;

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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