Misspecified Canonical Cointegrating Regressions

ABSTRACT Least squares (LS) estimation of canonical cointegrating regression (CCR) is explored allowing for potential misspecification from singularity in the system and multicointegration among the variables. The following properties are established: (i) superconsistent estimation with increased rates of convergence in degenerate directions; and (ii) limit theory that is nuisance parameter dependent in degenerate directions. Importantly for practitioners, inference for testing coefficient restrictions is shown to remain valid under certain conditions in CCR despite potential misspecification from singularity in the system.

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Publication Details

Journal
Journal of Time Series Analysis
Published
2026-10-19
DOI
https://doi.org/10.1111/jtsa.70082
Primary Topic
Advanced Statistical Methods and Models
Type
article
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article

Misspecified Canonical Cointegrating Regressions

Igor Kheifets, Peter C.B. Phillips
Journal of Time Series Analysis
Advanced Statistical Methods and Models
article

Misspecified Canonical Cointegrating Regressions

Igor Kheifets, Peter C.B. Phillips
article en

Abstract

ABSTRACT Least squares (LS) estimation of canonical cointegrating regression (CCR) is explored allowing for potential misspecification from singularity in the system and multicointegration among the variables. The following properties are established: (i) superconsistent estimation with increased rates of convergence in degenerate directions; and (ii) limit theory that is nuisance parameter dependent in degenerate directions. Importantly for practitioners, inference for testing coefficient restrictions is shown to remain valid under certain conditions in CCR despite potential misspecification from singularity in the system.

Journal of Time Series Analysis
University of North Carolina at Charlotte (US), University of Auckland (NZ), Yale University (US)
Openalex Percentile: Top 11%
Advanced Statistical Methods and Models
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