Cointegration: Its fatal flaw and a proposed solution

Summary: It has been argued that whenever regression models involve nonstationary and trending variables, estimation methods appropriate to stationary series cannot be applied to such models and instead require cointegration techniques. Unfortunately, extant methodologies applied to cointegration ar...

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Main Authors: P.A.V.B. Swamy, Peter von zur Muehlen
Format: Article
Language:English
Published: Elsevier 2020-01-01
Series:Sustainable Futures
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2666188820300319
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spelling doaj-b1095fc540e34afa82a4e48ab2dfb7392021-03-22T08:44:04ZengElsevierSustainable Futures2666-18882020-01-012100038Cointegration: Its fatal flaw and a proposed solutionP.A.V.B. Swamy0Peter von zur Muehlen1Corresponding author.; Federal Reserve Board (Retired), Washington, DC, United StatesFederal Reserve Board (Retired), Washington, DC, United StatesSummary: It has been argued that whenever regression models involve nonstationary and trending variables, estimation methods appropriate to stationary series cannot be applied to such models and instead require cointegration techniques. Unfortunately, extant methodologies applied to cointegration are a trap: if the error term of a cointegration regression consists of omitted relevant regressors, then, even though they are integrated to the same order, the dependent and the independent variables of the regression are not cointegrated! This paper proposes a remedy based on time-varying coefficient (TVC) modeling that overcomes all the shortcomings described in the paper.http://www.sciencedirect.com/science/article/pii/S2666188820300319Integrated variableCointegrated variablesCointegrating vectorNonstationary variableTime-varying coefficient model
collection DOAJ
language English
format Article
sources DOAJ
author P.A.V.B. Swamy
Peter von zur Muehlen
spellingShingle P.A.V.B. Swamy
Peter von zur Muehlen
Cointegration: Its fatal flaw and a proposed solution
Sustainable Futures
Integrated variable
Cointegrated variables
Cointegrating vector
Nonstationary variable
Time-varying coefficient model
author_facet P.A.V.B. Swamy
Peter von zur Muehlen
author_sort P.A.V.B. Swamy
title Cointegration: Its fatal flaw and a proposed solution
title_short Cointegration: Its fatal flaw and a proposed solution
title_full Cointegration: Its fatal flaw and a proposed solution
title_fullStr Cointegration: Its fatal flaw and a proposed solution
title_full_unstemmed Cointegration: Its fatal flaw and a proposed solution
title_sort cointegration: its fatal flaw and a proposed solution
publisher Elsevier
series Sustainable Futures
issn 2666-1888
publishDate 2020-01-01
description Summary: It has been argued that whenever regression models involve nonstationary and trending variables, estimation methods appropriate to stationary series cannot be applied to such models and instead require cointegration techniques. Unfortunately, extant methodologies applied to cointegration are a trap: if the error term of a cointegration regression consists of omitted relevant regressors, then, even though they are integrated to the same order, the dependent and the independent variables of the regression are not cointegrated! This paper proposes a remedy based on time-varying coefficient (TVC) modeling that overcomes all the shortcomings described in the paper.
topic Integrated variable
Cointegrated variables
Cointegrating vector
Nonstationary variable
Time-varying coefficient model
url http://www.sciencedirect.com/science/article/pii/S2666188820300319
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