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Social and Behavioral Sciences Commons

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Singapore Management University

Research Collection School Of Economics

2013

Endogeneity

Discipline

Articles 1 - 4 of 4

Full-Text Articles in Social and Behavioral Sciences

Predictive Regression Under Various Degrees Of Persistence And Robust Long-Horizon Regression, Peter C. B. Phillips, Ji Hyung Lee Dec 2013

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 …


Semiparametric Estimation In Triangular System Equations With Nonstationarity, Jiti Gao, Peter C. B. Phillips Sep 2013

Semiparametric Estimation In Triangular System Equations With Nonstationarity, Jiti Gao, Peter C. B. Phillips

Research Collection School Of Economics

A system of multivariate semiparametric nonlinear time series models is studied with possible dependence structures and nonstationarities in the parametric and nonparametric components. The parametric regressors may be endogenous while the nonparametric regressors are assumed to be strictly exogenous. The parametric regressors may be stationary or nonstationary and the nonparametric regressors are nonstationary integrated time series. Semiparametric least squares (SLS) estimation is considered and its asymptotic properties are derived. Due to endogeneity in the parametric regressors, SLS is not consistent for the parametric component and a semiparametric instrumental variable (SIV) method is proposed instead. Under certain regularity conditions, the SIV …


Inconsistent Var Regression With Common Explosive Roots, Peter C. B. Phillips, Tassos Magdalinos Aug 2013

Inconsistent Var Regression With Common Explosive Roots, Peter C. B. Phillips, Tassos Magdalinos

Research Collection School Of Economics

Nielsen (Working paper, University of Oxford, 2009) shows that vector autoregression is inconsistent when there are common explosive roots with geometric multiplicity greater than unity. This paper discusses that result, provides a coexplosive system extension and an illustrative example that helps to explain the finding, gives a consistent instrumental variable procedure, and reports some simulations. Some exact limit distribution theory is derived and a useful new reverse martingale central limit theorem is proved.


Local Linear Gmm Estimation Of Functional Coefficient Iv Models With Application To The Estimation Of Rate Of Return To Schooling, Liangjun Su, Irina Murtazashvili, Aman Ullah Apr 2013

Local Linear Gmm Estimation Of Functional Coefficient Iv Models With Application To The Estimation Of Rate Of Return To Schooling, Liangjun Su, Irina Murtazashvili, Aman Ullah

Research Collection School Of Economics

We consider the local linear GMM estimation of functional coe cient models with a mix of discrete and continuous data and in the presence of endogenous regressors. We establish the asymptotic normality of the estimator and derive the optimal instrumental variable that minimizes the asymptotic variance-covariance matrix among the class of all local linear GMM estimators. Data-dependent bandwidth sequences are also allowed for. We propose a nonparametric test for the constancy of the functional coefficients, study its asymptotic properties under the null hypothesis as well as a sequence of local alternatives and global alternatives, and propose a bootstrap version for …