High-dimensional IV cointegration estimation and inference

A semiparametric triangular systems approach shows how multicointegrating linkages occur naturally in an I(1) cointegrated regression model when the long run error variance matrix in the system is singular. Under such singularity, cointegrated I(1) systems embody a multicointegrated structure that m...

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Main Authors: PHILLIPS, Peter C. B., KHEIFETS, Igor L.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/soe_research/2710
https://ink.library.smu.edu.sg/context/soe_research/article/3709/viewcontent/HighD_IV_cointegration_av.pdf
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spelling sg-smu-ink.soe_research-37092024-01-04T06:55:27Z High-dimensional IV cointegration estimation and inference PHILLIPS, Peter C. B. KHEIFETS, Igor L. A semiparametric triangular systems approach shows how multicointegrating linkages occur naturally in an I(1) cointegrated regression model when the long run error variance matrix in the system is singular. Under such singularity, cointegrated I(1) systems embody a multicointegrated structure that makes them useful in many empirical settings. Earlier work shows that such systems may be analyzed and estimated without appealing to the associated I(2) system but with suboptimal convergence rates and potential asymptotic bias. The present paper develops a robust approach to estimation and inference of such systems using high dimensional IV methods that have appealing asymptotic properties like those known to apply in the optimal estimation of cointegrated systems (Phillips, 1991). The approach uses an extended version of high-dimensional trend IV (Phillips, 2006, 2014) estimation with deterministic orthonormal instruments. The methods and derivations involve new results on high-dimensional IV techniques and matrix normalization in the limit theory that are of independent interest. Wald tests of general linear restrictions are constructed using a fixed-b long run variance estimator that leads to robust pivotal HAR inference in both cointegrated and multicointegrated cases. Simulations show good properties of the estimation and inferential procedures in finite samples. An empirical illustration to housing stocks, starts and completions is provided. 2024-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2710 info:doi/10.1016/j.jeconom.2023.105622 https://ink.library.smu.edu.sg/context/soe_research/article/3709/viewcontent/HighD_IV_cointegration_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Cointegration HAR inference High-dimensional IV Long run variance matrix Multicointegration Singularity Trend IV estimation Econometrics Economic Theory
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Cointegration
HAR inference
High-dimensional IV
Long run variance matrix
Multicointegration
Singularity
Trend IV estimation
Econometrics
Economic Theory
spellingShingle Cointegration
HAR inference
High-dimensional IV
Long run variance matrix
Multicointegration
Singularity
Trend IV estimation
Econometrics
Economic Theory
PHILLIPS, Peter C. B.
KHEIFETS, Igor L.
High-dimensional IV cointegration estimation and inference
description A semiparametric triangular systems approach shows how multicointegrating linkages occur naturally in an I(1) cointegrated regression model when the long run error variance matrix in the system is singular. Under such singularity, cointegrated I(1) systems embody a multicointegrated structure that makes them useful in many empirical settings. Earlier work shows that such systems may be analyzed and estimated without appealing to the associated I(2) system but with suboptimal convergence rates and potential asymptotic bias. The present paper develops a robust approach to estimation and inference of such systems using high dimensional IV methods that have appealing asymptotic properties like those known to apply in the optimal estimation of cointegrated systems (Phillips, 1991). The approach uses an extended version of high-dimensional trend IV (Phillips, 2006, 2014) estimation with deterministic orthonormal instruments. The methods and derivations involve new results on high-dimensional IV techniques and matrix normalization in the limit theory that are of independent interest. Wald tests of general linear restrictions are constructed using a fixed-b long run variance estimator that leads to robust pivotal HAR inference in both cointegrated and multicointegrated cases. Simulations show good properties of the estimation and inferential procedures in finite samples. An empirical illustration to housing stocks, starts and completions is provided.
format text
author PHILLIPS, Peter C. B.
KHEIFETS, Igor L.
author_facet PHILLIPS, Peter C. B.
KHEIFETS, Igor L.
author_sort PHILLIPS, Peter C. B.
title High-dimensional IV cointegration estimation and inference
title_short High-dimensional IV cointegration estimation and inference
title_full High-dimensional IV cointegration estimation and inference
title_fullStr High-dimensional IV cointegration estimation and inference
title_full_unstemmed High-dimensional IV cointegration estimation and inference
title_sort high-dimensional iv cointegration estimation and inference
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/soe_research/2710
https://ink.library.smu.edu.sg/context/soe_research/article/3709/viewcontent/HighD_IV_cointegration_av.pdf
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