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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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 |
work_keys_str_mv |
AT pavbswamy cointegrationitsfatalflawandaproposedsolution AT petervonzurmuehlen cointegrationitsfatalflawandaproposedsolution |
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