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Robust Econometric Inference With Mixed Integrated And Mildly Explosive Regressors, Peter C. B. Phillips, Ji Hyung Lee
Robust Econometric Inference With Mixed Integrated And Mildly Explosive Regressors, Peter C. B. Phillips, Ji Hyung Lee
Research Collection School Of Economics
This paper explores in several prototypical models a convenient inference procedure for nonstationary variable regression that enables robust chi-square testing for a wide class of persistent and endogenous regressors. The approach uses the mechanism of self-generated instruments called IVX instrumentation developed by Magdalinos and Phillips (2009b). We first show that these methods remain valid for regressors with local unit roots in the explosive direction and mildly explosive roots, where the roots are further from unity in the explosive direction than 0 (n(-1)). It is also shown that Wald testing procedures remain robust for multivariate regressors with certain forms of mixed …
Predictive Regression Under Various Degrees Of Persistence And Robust Long-Horizon Regression, Peter C. B. Phillips, Ji Hyung Lee
Predictive Regression Under Various Degrees Of Persistence And Robust Long-Horizon Regression, Peter C. B. Phillips, Ji Hyung Lee
Research Collection School Of Economics
The paper proposes a novel inference procedure for long-horizon predictive regression with persistent regressors, allowing the autoregressive roots to lie in a wide vicinity of unity. The invalidity of conventional tests when regressors are persistent has led to a large literature dealing with inference in predictive regressions with local to unity regressors. Magdalinos and Phillips (2009b) recently developed a new framework of extended IV procedures (IVX) that enables robtist chi-square testing for a wider class of persistent regressors. We extend this robust procedure to an even wider parameter space in the vicinity of unity and apply the methods to long-horizon …
Standardized Lm Tests For Spatial Error Dependence In Linear Or Panel Regressions, Badi H. Baltagi, Zhenlin Yang
Standardized Lm Tests For Spatial Error Dependence In Linear Or Panel Regressions, Badi H. Baltagi, Zhenlin Yang
Research Collection School Of Economics
The robustness of the Lagrange Multiplier (LM) tests for spatial error dependence of Burridge (1980) and Born and Breitung (2011) for the linear regression model, and Anselin (1988) and Debarsy and Etur (2010) for the panel regression model with random or fixed effects are examined. While all tests are asymptotically robust against distributional mis‐specification, their finite sample behaviour may be sensitive to the spatial layout. To overcome this shortcoming, standardized LM tests are suggested. Monte Carlo results show that the new tests possess good finite sample properties. An important observation made throughout this study is that the LM tests for …
A Simple And Robust Method Of Inference For Spatial Lag Dependence, Zhenlin Yang, Yan Shen
A Simple And Robust Method Of Inference For Spatial Lag Dependence, Zhenlin Yang, Yan Shen
Research Collection School Of Economics
A simple and reliable method of inference for the spatial parameter in spatial autoregressive models is introduced, based on a statistic obtained by centering and rescaling the numerator of the concentrated Gaussian score function. The resulted tests and confidence intervals are robust against the distributional misspecifications and are insensitive to the spatial layouts and the error standard deviation. In contrast, the standard methods based on Gaussian score and information matrix may lead to inconsistent inference when errors are non normal, and can be quite sensitive to the spatial layouts and the error standard deviation even when errors are normally distributed. …
Standardized Lm Tests For Spatial Error Dependence In Linear Or Panel Regressions, Badi H. Baltagi, Zhenlin Yang
Standardized Lm Tests For Spatial Error Dependence In Linear Or Panel Regressions, Badi H. Baltagi, Zhenlin Yang
Research Collection School Of Economics
The robustness of the LM tests for spatial error dependence of Burridge (1980) for the linear regression model and Anselin (1988) for the panel regression model are examined. While both tests are asymptotic ally robust against distributional misspecification, their finite sample behavior can be sensitive to the spatial layout. To overcome this shortcoming, standardized LM tests are suggested. Monte Carlo results show that the new tests possess good finite sample properties. An important observation made throughout this study is that the LM tests for spatial dependence need to be both mean- and variance-adjusted for good finite sample performance to be …
A Robust Lm Test For Spatial Error Components, Zhenlin Yang
A Robust Lm Test For Spatial Error Components, Zhenlin Yang
Research Collection School Of Economics
This paper presents previous termanext term modified previous termLM test of spatial error components,next term which is shown to be previous termrobustnext term against distributional misspecifications and previous termspatialnext term layouts. The proposed previous termtestnext term differs from the previous termLM testnext term of Anselin (2001) by previous termanext term term in the denominators of the previous termtestnext term statistics. This term disappears when either the previous termerrorsnext term are normal, or the variance of the diagonal elements of the product of previous termspatialnext term weights matrix and its transpose is zero or approaches to zero as sample size goes …
A Robust Lm Test For Spatial Error Components, Zhenlin Yang
A Robust Lm Test For Spatial Error Components, Zhenlin Yang
Research Collection School Of Economics
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 approaches 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 …
On Joint Modelling And Testing For Local And Global Spatial Externalities, Zhenlin Yang
On Joint Modelling And Testing For Local And Global Spatial Externalities, Zhenlin Yang
Research Collection School Of Economics
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 …