Structural Nonparametric Cointegrating Regression

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is kno...

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Main Authors: Wang, Q. Y., Peter C. B. PHILLIPS
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Language:English
Published: Institutional Knowledge at Singapore Management University 2009
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Online Access:https://ink.library.smu.edu.sg/soe_research/1813
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spelling sg-smu-ink.soe_research-28122016-05-13T01:54:22Z Structural Nonparametric Cointegrating Regression Wang, Q. Y. Peter C. B. PHILLIPS, Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill-posed inverse problems. In functional cointegrating regressions where the regressor is an integrated or near-integrated time series, it is shown here that inverse and ill-posed inverse problems do not arise. Instead, simple nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution theory is mixed normal, giving straightforward asymptotics that are useable in practical work. It is further shown that use of augmented regression, as is common in linear cointegration modeling to address endogeneity, does not lead to bias reduction in nonparametric regression, but there is an asymptotic gain in variance reduction. The results provide a convenient basis for inference in structural nonparametric regression with nonstationary time series when there is a single integrated or near-integrated regressor. The methods may be applied to a range of empirical models where functional estimation of cointegrating relations is required. 2009-11-01T07:00:00Z text https://ink.library.smu.edu.sg/soe_research/1813 info:doi/10.3982/ECTA7732 Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Brownian local time Cointegration Functional regression Gaussian process Integrated process Kernel estimate Near integration Nonlinear functional Nonparametric regression Structural estimation Unit root Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Brownian local time
Cointegration
Functional regression
Gaussian process
Integrated process
Kernel estimate
Near integration
Nonlinear functional
Nonparametric regression
Structural estimation
Unit root
Econometrics
spellingShingle Brownian local time
Cointegration
Functional regression
Gaussian process
Integrated process
Kernel estimate
Near integration
Nonlinear functional
Nonparametric regression
Structural estimation
Unit root
Econometrics
Wang, Q. Y.
Peter C. B. PHILLIPS,
Structural Nonparametric Cointegrating Regression
description Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill-posed inverse problems. In functional cointegrating regressions where the regressor is an integrated or near-integrated time series, it is shown here that inverse and ill-posed inverse problems do not arise. Instead, simple nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution theory is mixed normal, giving straightforward asymptotics that are useable in practical work. It is further shown that use of augmented regression, as is common in linear cointegration modeling to address endogeneity, does not lead to bias reduction in nonparametric regression, but there is an asymptotic gain in variance reduction. The results provide a convenient basis for inference in structural nonparametric regression with nonstationary time series when there is a single integrated or near-integrated regressor. The methods may be applied to a range of empirical models where functional estimation of cointegrating relations is required.
format text
author Wang, Q. Y.
Peter C. B. PHILLIPS,
author_facet Wang, Q. Y.
Peter C. B. PHILLIPS,
author_sort Wang, Q. Y.
title Structural Nonparametric Cointegrating Regression
title_short Structural Nonparametric Cointegrating Regression
title_full Structural Nonparametric Cointegrating Regression
title_fullStr Structural Nonparametric Cointegrating Regression
title_full_unstemmed Structural Nonparametric Cointegrating Regression
title_sort structural nonparametric cointegrating regression
publisher Institutional Knowledge at Singapore Management University
publishDate 2009
url https://ink.library.smu.edu.sg/soe_research/1813
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