Nonparametric Cointegrating Regression with Endogeneity and Long Memory

This paper explores nonparametric estimation, inference, and specification testing in a nonlinear cointegrating regression model where the structural equation errors are serially dependent and where the regressor is endogenous and may be driven by long memory innovations. Generalizing earlier result...

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Main Authors: WANG, Qiying, PHILLIPS, Peter C. B.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/soe_research/1843
https://ink.library.smu.edu.sg/context/soe_research/article/2842/viewcontent/NonparaetricCointregatingRegEndoLongMemory_2014_pp.pdf
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spelling sg-smu-ink.soe_research-28422020-01-15T02:05:24Z Nonparametric Cointegrating Regression with Endogeneity and Long Memory WANG, Qiying PHILLIPS, Peter C. B. This paper explores nonparametric estimation, inference, and specification testing in a nonlinear cointegrating regression model where the structural equation errors are serially dependent and where the regressor is endogenous and may be driven by long memory innovations. Generalizing earlier results of Wang and Phillips (2009a, b, Econometric Theory 25, 710-738, Econometrica 77, 1901-1948), the conventional nonparametric local level kernel estimator is shown to be consistent and asymptotically (mixed) normal in these cases, thereby opening up inference by conventional nonparametric methods to a wide class of potentially nonlinear cointegrated relations. New results on the consistency of parametric estimates in nonlinear cointegrating regressions are provided, extending earlier research on parametric nonlinear regression and providing primitive conditions for parametric model testing. A model specification test is studied and confirmed to provide a valid mechanism for testing parametric specifications that is robust to endogeneity. But under long memory innovations the test is not pivotal, its convergence rate is parameter dependent, and its limit theory involves the local time of fractional Brownian motion. Simulation results show good performance for the nonparametric kernel estimates in cases of strong endogeneity and long memory, whereas the specification test is shown to be sensitive to the presence of long memory innovations, as predicted by asymptotic theory. 2016-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1843 info:doi/10.1017/S0266466614000917 https://ink.library.smu.edu.sg/context/soe_research/article/2842/viewcontent/NonparaetricCointregatingRegEndoLongMemory_2014_pp.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University integrated time-series asymptotic theory whittle estimation inference models heteroskedasticity convergence functionals tests Econometrics Economics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic integrated time-series
asymptotic theory
whittle estimation
inference
models
heteroskedasticity
convergence
functionals
tests
Econometrics
Economics
spellingShingle integrated time-series
asymptotic theory
whittle estimation
inference
models
heteroskedasticity
convergence
functionals
tests
Econometrics
Economics
WANG, Qiying
PHILLIPS, Peter C. B.
Nonparametric Cointegrating Regression with Endogeneity and Long Memory
description This paper explores nonparametric estimation, inference, and specification testing in a nonlinear cointegrating regression model where the structural equation errors are serially dependent and where the regressor is endogenous and may be driven by long memory innovations. Generalizing earlier results of Wang and Phillips (2009a, b, Econometric Theory 25, 710-738, Econometrica 77, 1901-1948), the conventional nonparametric local level kernel estimator is shown to be consistent and asymptotically (mixed) normal in these cases, thereby opening up inference by conventional nonparametric methods to a wide class of potentially nonlinear cointegrated relations. New results on the consistency of parametric estimates in nonlinear cointegrating regressions are provided, extending earlier research on parametric nonlinear regression and providing primitive conditions for parametric model testing. A model specification test is studied and confirmed to provide a valid mechanism for testing parametric specifications that is robust to endogeneity. But under long memory innovations the test is not pivotal, its convergence rate is parameter dependent, and its limit theory involves the local time of fractional Brownian motion. Simulation results show good performance for the nonparametric kernel estimates in cases of strong endogeneity and long memory, whereas the specification test is shown to be sensitive to the presence of long memory innovations, as predicted by asymptotic theory.
format text
author WANG, Qiying
PHILLIPS, Peter C. B.
author_facet WANG, Qiying
PHILLIPS, Peter C. B.
author_sort WANG, Qiying
title Nonparametric Cointegrating Regression with Endogeneity and Long Memory
title_short Nonparametric Cointegrating Regression with Endogeneity and Long Memory
title_full Nonparametric Cointegrating Regression with Endogeneity and Long Memory
title_fullStr Nonparametric Cointegrating Regression with Endogeneity and Long Memory
title_full_unstemmed Nonparametric Cointegrating Regression with Endogeneity and Long Memory
title_sort nonparametric cointegrating regression with endogeneity and long memory
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/soe_research/1843
https://ink.library.smu.edu.sg/context/soe_research/article/2842/viewcontent/NonparaetricCointregatingRegEndoLongMemory_2014_pp.pdf
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