Fully modified least squares cointegrating parameter estimation in multicointegrated systems

Multicointegration is traditionally defined as a particular long run relationship among variables in a parametric vector autoregressive model that introduces additional cointegrating links between these variables and partial sums of the equilibrium errors. This paper departs from the parametric mode...

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Main Authors: KHEIFETS, Igor L., PHILLIPS, Peter C. B.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/soe_research/2691
https://ink.library.smu.edu.sg/context/soe_research/article/3690/viewcontent/Fully_modifiedLSC_av.pdf
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spelling sg-smu-ink.soe_research-36902023-10-27T08:41:01Z Fully modified least squares cointegrating parameter estimation in multicointegrated systems KHEIFETS, Igor L. PHILLIPS, Peter C. B. Multicointegration is traditionally defined as a particular long run relationship among variables in a parametric vector autoregressive model that introduces additional cointegrating links between these variables and partial sums of the equilibrium errors. This paper departs from the parametric model, using a semiparametric formulation that reveals the explicit role that singularity of the long run conditional covariance matrix plays in determining multicointegration. The semiparametric framework has the advantage that short run dynamics do not need to be modeled and estimation by standard techniques such as fully modified least squares (FM-OLS) on the original system is straightforward. The paper derives FM-OLS limit theory in the multicointegrated setting, showing how faster rates of convergence are achieved in the direction of singularity and that the limit distribution depends on the distribution of the conditional one-sided long run covariance estimator used in FM-OLS estimation. Wald tests of restrictions on the regression coefficients have nonstandard limit theory which depends on nuisance parameters in general. The usual tests are shown to be conservative when the restrictions are isolated to the directions of singularity and, under certain conditions, are invariant to singularity otherwise. Simulations show that approximations derived in the paper work well in finite samples. The findings are illustrated empirically in an analysis of fiscal sustainability of the US government over the post-war period. 2023-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2691 info:doi/10.1016/j.jeconom.2021.07.002 https://ink.library.smu.edu.sg/context/soe_research/article/3690/viewcontent/Fully_modifiedLSC_av.pdf http://creativecommons.org/licenses/by/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University cointegration multicointegration fully modified regression singular long run variance matrix degenerate Wald test fiscal sustainability Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic cointegration
multicointegration
fully modified regression
singular long run variance matrix
degenerate Wald test
fiscal sustainability
Econometrics
spellingShingle cointegration
multicointegration
fully modified regression
singular long run variance matrix
degenerate Wald test
fiscal sustainability
Econometrics
KHEIFETS, Igor L.
PHILLIPS, Peter C. B.
Fully modified least squares cointegrating parameter estimation in multicointegrated systems
description Multicointegration is traditionally defined as a particular long run relationship among variables in a parametric vector autoregressive model that introduces additional cointegrating links between these variables and partial sums of the equilibrium errors. This paper departs from the parametric model, using a semiparametric formulation that reveals the explicit role that singularity of the long run conditional covariance matrix plays in determining multicointegration. The semiparametric framework has the advantage that short run dynamics do not need to be modeled and estimation by standard techniques such as fully modified least squares (FM-OLS) on the original system is straightforward. The paper derives FM-OLS limit theory in the multicointegrated setting, showing how faster rates of convergence are achieved in the direction of singularity and that the limit distribution depends on the distribution of the conditional one-sided long run covariance estimator used in FM-OLS estimation. Wald tests of restrictions on the regression coefficients have nonstandard limit theory which depends on nuisance parameters in general. The usual tests are shown to be conservative when the restrictions are isolated to the directions of singularity and, under certain conditions, are invariant to singularity otherwise. Simulations show that approximations derived in the paper work well in finite samples. The findings are illustrated empirically in an analysis of fiscal sustainability of the US government over the post-war period.
format text
author KHEIFETS, Igor L.
PHILLIPS, Peter C. B.
author_facet KHEIFETS, Igor L.
PHILLIPS, Peter C. B.
author_sort KHEIFETS, Igor L.
title Fully modified least squares cointegrating parameter estimation in multicointegrated systems
title_short Fully modified least squares cointegrating parameter estimation in multicointegrated systems
title_full Fully modified least squares cointegrating parameter estimation in multicointegrated systems
title_fullStr Fully modified least squares cointegrating parameter estimation in multicointegrated systems
title_full_unstemmed Fully modified least squares cointegrating parameter estimation in multicointegrated systems
title_sort fully modified least squares cointegrating parameter estimation in multicointegrated systems
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/soe_research/2691
https://ink.library.smu.edu.sg/context/soe_research/article/3690/viewcontent/Fully_modifiedLSC_av.pdf
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