2022
DOI: 10.1017/s0266466622000342
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Estimation and Inference With Near Unit Roots

Abstract: New methods are developed for identifying, estimating, and performing inference with nonstationary time series that have autoregressive roots near unity. The approach subsumes unit-root (UR), local unit-root (LUR), mildly integrated (MI), and mildly explosive (ME) specifications in the new model formulation. It is shown how a new parameterization involving a localizing rate sequence that characterizes departures from unity can be consistently estimated in all cases. Simple pivotal limit distributions that enab… Show more

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Cited by 13 publications
(10 citation statements)
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“…While models with unit roots provide a prototypical framework for capturing persistence in time series data, these models have modifications designed to capture a wider class of time series behavior in which the autoregressive roots are not restricted to unity as they are with integrated processes. An important subclass of more general models with near unit roots (Phillips, 2023) is the class of LUR models…”
Section: Local Unit Root Processesmentioning
confidence: 99%
“…While models with unit roots provide a prototypical framework for capturing persistence in time series data, these models have modifications designed to capture a wider class of time series behavior in which the autoregressive roots are not restricted to unity as they are with integrated processes. An important subclass of more general models with near unit roots (Phillips, 2023) is the class of LUR models…”
Section: Local Unit Root Processesmentioning
confidence: 99%
“…Similar robustness is achieved in Magdalinos and Petrova (2021) where the authors developed a testing procedure based on instrumental variable estimation and pre-testing for stationary or explosive direction. Phillips (2021) considered estimation and inference of localizing parameters c and α in the representation ρ T = 1 + c/T α . Considering possibly explosive sample intervals in time series (just 4 years before the end of the sample), Phillips (2021) argued that the method may be seen as an alternative of recursive bubble identification methods.…”
Section: Asymptotics For Autoregressive Parametermentioning
confidence: 99%
“…Phillips (2021) considered estimation and inference of localizing parameters c and α in the representation ρ T = 1 + c/T α . Considering possibly explosive sample intervals in time series (just 4 years before the end of the sample), Phillips (2021) argued that the method may be seen as an alternative of recursive bubble identification methods.…”
Section: Asymptotics For Autoregressive Parametermentioning
confidence: 99%
See 1 more Smart Citation
“…See Chan and Wei (1987), Phillips (1987Phillips ( , 1988, Phillips and Magdalinos (2007), Aue and Horváth (2007), Andrews and Guggenberger (2008), Buchmann and Chan (2013), Miao et al (2015), Jiang et al (2022), Tanaka (2017), and references therein. In particular, we refer to Stock (1991) for the empirical research or Phillips (2021) for the recent theoretical progress and empirical research on the processes with near unit roots. are complementary to the results of the OLS estimators established by Giraitis and Phillips (2006) and Phillips and Magdalinos (2007).…”
Section: Introductionmentioning
confidence: 99%