LM tests of spatial dependence based on bootstrap critical values

Ministry of Education, Singapore under its Academic Research Funding Tier 1Published in Journal of Econometrics https://doi.org/10.1016/j.jeconom.2014.10.005</p

A Robust LM Test for Spatial Error Components

This paper presents a modified LM test of spatial error components, which is shown to be robust against distributional misspecifications and spatial layouts. The proposed test differs from the LM test of Anselin (2001) by a term in the denominators of the test statistics. This term disappears when either the errors are normal, or the variance of the diagonal elements of the product of spatial weights matrix and its transpose is zero or approaching to zero as sample size goes large. When neither is true, as is often the case in practice, the effect of this term can be significant even when sample size is large. As a result, there can be severe size distortions of the Anselins LM test, a phenomenon revealed by the Monte Carlo results of Anselin and Moreno (2003) and further confirmed by the Monte Carlo results presented in this paper. Our Monte Carlo results also show that the proposed test performs well in general.Distributional misspecification, Robustness, Spatial layouts, Spatial error components, LM tests

A Robust LM Test for Spatial Error Components

This paper presents a modified LM test of spatial error components, which is shown to be robust against distributional misspecifications and spatial layouts. The proposed test differs from the LM test of Anselin (2001) by a term in the denominators of the test statistics. This term disappears when either the errors are normal, or the variance of the diagonal elements of the product of spatial weights matrix and its transpose is zero or approaching to zero as sample size goes large. When neither is true, as is often the case in practice, the effect of this term can be significant even when sample size is large. As a result, there can be severe size distortions of the Anselin’s LM test, a phenomenon revealed by the Monte Carlo results of Anselin and Moreno (2003) and further confirmed by the Monte Carlo results presented in this paper. Our Monte Carlo results also show that the proposed test performs well in general.Distributional misspecification; Robustness, Spatial layouts; Spatial error components; LM tests

On Joint Modelling and Testing for Local and Global Spatial Externalities

This paper concerns the joint modeling, estimation and testing for local and global spatial externalities. Spatial externalities have become in recent years a standard notion of economic research activities in relation to social interactions, spatial spillovers and dependence, etc., and have received an increasing attention by econometricians and applied researchers. While conceptually the principle underlying the spatial dependence is straightforward, the precise way in which this dependence should be included in a regression model is complex. Following the taxonomy of Anselin (2003, International Regional Science Review 26, 153-166), a general model is proposed, which takes into account jointly local and global externalities in both modelled and unmodelled effects. The proposed model encompasses all the models discussed in Anselin (2003). Robust methods of estimation and testing are developed based on Gaussian quasi-likelihood. Large and small sample properties of the proposed methods are investigated.Asymptotic property, Finite sample property, Quasi-likelihood, Spatial regression models, Robustness, Tests of spatial externalities

On Joint Modelling and Testing for Local and Global Spatial Externalities

This paper concerns the joint modeling, estimation and testing for local and global spatial externalities. Spatial externalities have become in recent years a standard notion of economic research activities in relation to social interactions, spatial spillovers and dependence, etc., and have received an increasing attention by econometricians and applied researchers. While conceptually the principle underlying the spatial dependence is straightforward, the precise way in which this dependence should be included in a regression model is complex. Following the taxonomy of Anselin (2003, International Regional Science Review 26, 153-166), a general model is proposed, which takes into account jointly local and global externalities in both modelled and unmodelled effects. The proposed model encompasses all the models discussed in Anselin (2003). Robust methods of estimation and testing are developed based on Gaussian quasi-likelihood. Large and small sample properties of the proposed methods are investigated.Asymptotic property, Finite sample property, Quasi-likelihood, Spatial regression models, Robustness, Tests of spatial externalities.

Instrumental Variable Quantile Estimation of Spatial Autoregressive Models

We propose an instrumental variable quantile regression (IVQR) estimator for spatial autoregressive (SAR) models. Like the GMM estimators of Lin and Lee (2006) and Kelejian and Prucha (2006), the IVQR estimator is robust against heteroscedasticity. Unlike the GMM estimators, the IVQR estimator is also robust against outliers and requires weaker moment conditions. More importantly, it allows us to characterize the heterogeneous impact of variables on different points (quantiles) of a response distribution. We derive the limiting distribution of the new estimator. Simulation results show that the new estimator performs well in finite samples at various quantile points. In the special case of median restriction, it outperforms the conventional QML estimator without taking into account of heteroscedasticity in the errors; it also outperforms the GMM estimators with or without considering the heteroscedasticity.Spatial Autoregressive Model, Quantile Regression, Instrumental Variable, Quasi Maximum Likelihood, GMM, Robustness

Determinants of Job Turnover Intentions: Evidence from Singapore

This paper explores both observable and unobservable variables that would affect employed workers’ decisions on job change. We find that age, job satisfaction, satisfaction with working environment or job security, and firm size are among the major factors determining workers’ intentions of job-to-job mobility. Younger workers and workers in smaller firms are more likely to look for other jobs. We also find that men are more likely to consider a change in job than women, but when “actually looking for another job” is concerned, men and women do not differ. Furthermore, monthly income and working sector contribute significantly to looking for other jobs.Voluntary job-to-job mobility; Job satisfaction; Logistic regression model

Asymptotics and Bootstrap for Transformed Panel Data Regressions

This paper investigates the asymptotic properties of quasi-maximum likelihood estimators for transformed random effects models where both the response and (some of) the covariates are subject to transformations for inducing normality, flexible functional form, homoscedasticity, and simple model structure. We develop a quasi maximum likelihood-type procedure for model estimation and inference. We prove the consistency and asymptotic normality of the parameter estimates, and propose a simple bootstrap procedure that leads to a robust estimate of the variance-covariance matrix. Monte Carlo results reveal that these estimates perform well in finite samples, and that the gains by using bootstrap procedure for inference can be enormous.Asymptotics, Bootstrap, Quasi-MLE, Transformed panels, Variance-covariance matrix estimate