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

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  • Gianfranco Piras
  • Ingmar R. Prucha

Abstract

This paper explores the properties of pre-test 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 and the empirical size of tests can differ substantially from the nominal size. This is in contrast to the ML estimator. However, if the true data generating process corresponds to the spatial error or lag model the issues arising with the pre-test estimators seem to be lessened.

Suggested Citation

  • Gianfranco Piras & Ingmar R. Prucha, 2014. "On the Finite Sample Properties of Pre-Test Estimators of Spatial Models," CESifo Working Paper Series 4725, CESifo.
  • Handle: RePEc:ces:ceswps:_4725
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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