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Journal of Time Series Analysis
Article . 2024 . Peer-reviewed
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Article . 2025
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SSRN Electronic Journal
Article . 2022 . Peer-reviewed
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Self‐normalization inference for linear trends in cointegrating regressions

Self-normalization inference for linear trends in cointegrating regressions
Authors: Cheol‐Keun Cho;

Self‐normalization inference for linear trends in cointegrating regressions

Abstract

In this article, statistical tests concerning the trend coefficient in cointegrating regressions are addressed for the case when the stochastic regressors have deterministic linear trends. The self‐normalization (SN) approach is adopted for developing inferential methods in the integrated and modified ordinary least squares (IMOLS) estimation framework. Two different self‐normalizers are used to construct the SN test statistics: a functional of the recursive IMOLS estimators and a functional of the IMOLS residuals. These two self‐normalizers produce two SN tests, denoted by and respectively. Neither test requires studentization with a heteroskedasticity and autocorrelation consistent (HAC) estimator. A trimming parameter must be chosen to implement the test, whereas the test does not require any tuning parameter. In the simulation, the test exhibits the smallest size distortion among the inferential methods examined in this article. However, this may come with some loss of power, particularly in small samples.

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Keywords

linear trend, cointegration, Asymptotic distribution theory in statistics, HAC, trending regressors, self-normalization, Economic time series analysis, Time series, auto-correlation, regression, etc. in statistics (GARCH), Inference from stochastic processes, fixed-\(b\), IMOLS

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
hybrid