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Replication Data for: The Wald Test of Common Factors in Spatial Model Specification Search Strategies

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: The Wald Test of Common Factors in Spatial Model Specification Search Strategies
 
Identifier https://doi.org/10.7910/DVN/CY7YWE
 
Creator Juhl, Sebastian
 
Publisher Harvard Dataverse
 
Description Distinguishing substantively meaningful spillover effects from correlated residuals is of great importance in cross-sectional studies. Both forms of spatial dependence not only hold different implications for the choice of an unbiased estimator but also for the validity of inferences. To guide model specification, different empirical strategies involve the estimation of an unrestricted spatial Durbin model and subsequently use the Wald test to scrutinize the non-linear restriction of common factors implied by pure error dependence. However, the Wald test's sensitivity to algebraically equivalent formulations of the null hypothesis receives scant attention in the context of cross-sectional analyses. This article shows analytically that the non-invariance of the Wald test to such reparameterizations stems from the application of a Taylor series expansion to approximate the restriction's sampling distribution. While asymptotically valid, Monte Carlo simulations reveal that alternative formulations of the common factor restriction frequently produce conflicting conclusions in finite samples. An empirical example illustrates the substantive implications of this problem. Consequently, researchers should either base inferences on bootstrap critical values for the Wald statistic or use the likelihood ratio test which is invariant to such reparameterizations when deciding on the model specification that adequately reflects the spatial process generating the data.
 
Subject Social Sciences
Spillover Effects
Common Factors
Wald Test
Spatial Econometrics
 
Contributor Code Ocean