Robinson, Peter M. and Rossi, Francesca (2012): Improved tests for spatial correlation.

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Abstract
We consider testing the null hypothesis of no spatial autocorrelation against the alternative of first order spatial autoregression. A Wald test statistic has good first order asymptotic properties, but these may not be relevant in small or moderatesized samples, especially as (depending on properties of the spatial weight matrix) the usual parametric rate of convergence may not be attained. We thus develop tests with more accurate size properties, by means of Edgeworth expansions and the bootstrap. The finitesample performance of the tests is examined in Monte Carlo simulations.
Item Type:  MPRA Paper 

Original Title:  Improved tests for spatial correlation 
Language:  English 
Keywords:  Spatial Autocorrelation; Ordinary Least Squares; Hypothesis Testing; Edgeworth Expansion; Bootstrap 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C12  Hypothesis Testing: General C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C21  CrossSectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions 
Item ID:  41835 
Depositing User:  Francesca Rossi 
Date Deposited:  09 Oct 2012 20:08 
Last Modified:  15 Oct 2019 04:24 
References:  Case, A.C. (1991). Spatial Patterns in Household Demand. Econometrica, 59, 95365. Forchini, G. (2002). The Exact Cumulative Distribution Function of a Ratio of Quadratic Forms in Normal Variables, with Applications to the AR(1) Model. Econometric Theory 18, 82352. Hall, P. (1992a). The Bootstrap and Edgeworth Expansion. SpringerVerlag. Hall, P. (1992b). On the Removal of Skewness by Transformation. Journal of the Royal Statistical Society. Series B 54, 22128. Hillier, G. (2001). The Density of a Quadratic Form in a Vector Uniformly Distributed on the nSphere. Econometric Theory 17, 128. Imhof, J.P. (1961). Computing the Distribution of Quadratic Forms in Normal Variables. Biometrika, 48, 26683. Kelejian, H.H. and I.R. Prucha (1998). A Generalized Spatial TwoStages Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances. Journal of Real Estate Finance and Economics 17, 99121. Lee, L.F. (2002). Consistency and Efficiency of Least Squares Estimation for Mixed Regressive, Spatial Autoregressive Models. Econometric theory 18, 25277. Lee, L.F. (2004). Asymptotic Distribution of QuasiMaximum Likelihood Estimates for Spatial Autoregressive Models. Econometrica 72, 18991925. Lu, Z.H. and M.L.King (2002). Improving the Numerical Techniques for Computing the Accumulated Distribution of a Quadratic Form in Normal Variables. Econometric Reviews 21, 14965. Paparoditis, E. and D.N. Politis (2005). Bootstrap Hypothesis Testing in Regression Models. Statistics & Probability Letters 74, 35665. Phillips, P.C.B. (1977). Approximations to Some Finite Sample Distributions Associated with a FirstOrder Stochastic Difference Equation. Econometrica, 45, 46385. Singh, K. (1981). On the Asymptotic Accuracy of Efron's Bootstrap. Annals of Statistics, 9, 118795. Yanagihara, H. and K. Yuan (2005). Four Improved Statistics for Contrasting Means by Correcting Skewness and Kurtosis. British Journal of Mathematical and Statistical Psychology 58, 20937. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/41835 