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Articles 1 - 13 of 13
Full-Text Articles in Social and Behavioral Sciences
Inference In Partially Identified Panel Data Models With Interactive Fixed Effects, Shengjie Hong, Liangjun Su, Yaqi Wang
Inference In Partially Identified Panel Data Models With Interactive Fixed Effects, Shengjie Hong, Liangjun Su, Yaqi Wang
Research Collection School Of Economics
This paper develops methods for statistical inferences in a partially identified nonparametric panel data model with endogeneity and interactive fixed effects. We consider the case where the number of cross-sectional units (N) is large and the number of time series periods (T).as well as the number of unobserved common factors (R) are fixed. Under some normalization rules, wecan concentrateout thelarge dimen-sional parameter vector of factor loadings and specify a set of conditional moment restriction that are involved with only the finite dimensional factor parameters along with the infinite dimensional nonpara-metric component. For a conjectured restriction on the parameter, we consider …
Structural Inference From Reduced Forms With Many Instruments, Peter C. B. Phillips, Wayne Yuan Gao
Structural Inference From Reduced Forms With Many Instruments, Peter C. B. Phillips, Wayne Yuan Gao
Research Collection School Of Economics
This paper develops exact finite sample and asymptotic distributions for structural equation tests based on partially restricted reduced form estimates. Particular attention is given to models with large numbers of instruments, wherein the use of partially restricted reduced form estimates is shown to be especially advantageous in statistical testing even in cases of uniformly weak instruments. Comparisons are made with methods based on unrestricted reduced forms, and numerical computations showing finite sample performance of the tests are reported. Some new results are obtained on inequalities between noncentral chi-squared distributions with different degrees of freedom that assist in analytic power comparisons.
Reduced Forms And Weak Instrumentation, Peter C. B. Phillips
Reduced Forms And Weak Instrumentation, Peter C. B. Phillips
Research Collection School Of Economics
This paper develops exact finite sample and asymptotic distributions for a class of reduced form estimators and predictors, allowing for the presence of unidentified or weakly identified structural equations. Weak instrument asymptotic theory is developed directly from finite sample results, unifying earlier findings and showing the usefulness of structural information in making predictions from reduced form systems in applications. Asymptotic results are reported for predictions from models with many weak instruments. Of particular interest is the finding that, in unidentified and weakly identified structural models, partially restricted reduced form predictors have considerably smaller forecast mean square errors than unrestricted reduced …
Estimating Smooth Structural Change In Cointegration Models, Peter C. B. Phillips, Degui Li, Jiti Gao
Estimating Smooth Structural Change In Cointegration Models, Peter C. B. Phillips, Degui Li, Jiti Gao
Research Collection School Of Economics
This paper studies nonlinear cointegration models in which the structural coefficients may evolve smoothly over time, and considers time-varying coefficient functions estimated by nonparametric kernel methods. It is shown that the usual asymptotic methods of kernel estimation completely break down in this setting when the functional coefficients are multivariate. The reason for this breakdown is a kernel induced degeneracy in the weighted signal matrix associated with the nonstationary regressors, a new phenomenon in the kernel regression literature. Some new techniques are developed to address the degeneracy and resolve the asymptotics, using a path-dependent local coordinate transformation to reorient coordinates and …
Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshino
Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshino
Research Collection School Of Economics
In this paper we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We estimate the functional coefficients by the sieve-IVQR technique and establish the uniform consistency and asymptotic normality of the estimators. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the functional coefficients and study its asymptotic. We conduct simulations to evaluate the finite sample behavior of our estimator and test statistic, and apply our method to study the estimation of quantile Engel curves.
Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshina
Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshina
Research Collection School Of Economics
In this paper, we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We approximate the unknown functional coefficients by some basis functions and estimate them by the IVQR technique. We establish the uniform consistency and asymptotic normality of the estimators of the functional coefficients. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the functional coefficients, study its asymptotic properties under the null hypothesis, a sequence of local alternatives and global alternatives, and propose a wild-bootstrap procedure …
Additive Nonparametric Regression In The Presence Of Endogenous Regressors, Deniz Ozabaci, Daniel J. Henderson, Liangjun Su
Additive Nonparametric Regression In The Presence Of Endogenous Regressors, Deniz Ozabaci, Daniel J. Henderson, Liangjun Su
Research Collection School Of Economics
In this article we consider nonparametric estimation of a structural equation model under full additivity constraint. We propose estimators for both the conditional mean and gradient which are consistent, asymptotically normal, oracle efficient, and free from the curse of dimensionality. Monte Carlo simulations support the asymptotic developments. We employ a partially linear extension of our model to study the relationship between child care and cognitive outcomes. Some of our (average) results are consistent with the literature (e.g., negative returns to child care when mothers have higher levels of education). However, as our estimators allow for heterogeneity both across and within …
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 …
Semiparametric Estimation In Triangular System Equations With Nonstationarity, Jiti Gao, Peter C. B. Phillips
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
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
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 …
Non-Parametric Regression Under Location Shifts, Peter C. B. Phillips, Liangjun Su
Non-Parametric Regression Under Location Shifts, Peter C. B. Phillips, Liangjun Su
Research Collection School Of Economics
Recent work by Wang and Phillips (2009b, 2011) has shown that ill-posed inverse problems do not arise in non-stationary non-parametric regression and there is no need for non-parametric instrumental variable estimation. Instead, simple Nadaraya–Watson non-parametric estimation of a cointegrating regression equation is consistent irrespective of the endogeneity in the regressor. The present paper shows that some closely related results apply in the case of structural non-parametric regression with independent data when there are continuous location shifts in the regressor. Some interesting cases are discovered where non-parametric regression is consistent, whereas parametric regression is inconsistent even when the true regression functional …
A Paradox Of Inconsistent Parametric And Consistent Nonparametric Regression, Peter C. B. Phillips, Liangjun Su
A Paradox Of Inconsistent Parametric And Consistent Nonparametric Regression, Peter C. B. Phillips, Liangjun Su
Research Collection School Of Economics
This paper explores a paradox discovered in recent work by Phillips and Su (2009). That paper gave an example in which nonparametric regression is consistent whereas parametric regression is inconsistent even when the true regression functional form is known and used in regression. This appears to be a paradox, as knowing the true functional form should not in general be detrimental in regression. In the present case, local regression methods turn out to have a distinct advantage because of endogeneity in the regressor. The paradox arises because additional correct information is not necessarily advantageous when information is incomplete. In the …