Point optimal testing with roots that are functionally local to unity
Point optimal testing with roots that are functionally local to unity
Limit theory for regressions involving local to unit roots (LURs) is now used extensively in time series econometric work, establishing power properties for unit root and cointegration tests, assisting the construction of uniform confidence intervals for autoregressive coefficients, and enabling the development of methods robust to departures from unit roots. The present paper shows how to generalize LUR asymptotics to cases where the localized departure from unity is a time varying function rather than a constant. Such a functional local unit root (FLUR) model has much greater generality and encompasses many cases of additional interest that appear in practical work, including structural break formulations that admit subperiods of unit root, local stationary and local explosive behavior within a given sample. Point optimal FLUR tests are constructed in the paper to accommodate such cases and demonstrate how the power envelope changes in situations of practical interest. Against FLUR alternatives, conventional constant point optimal tests can be asymptotically infinitely deficient in power, with poor finite sample power perfor- mance particularly when the departure from unity occurs early in the sample period. New analytic explanation for this phenomenon is provided. Simulation results are reported and some implications for empirical practice are examined
Functional local unit root, Local to unity, Uniform confidence interval, Unit root model
231-259
Bykhovskaya, Anna
a010627a-c30d-4b8e-ac97-7550f92646f9
Phillips, Peter
f67573a4-fc30-484c-ad74-4bbc797d7243
December 2020
Bykhovskaya, Anna
a010627a-c30d-4b8e-ac97-7550f92646f9
Phillips, Peter
f67573a4-fc30-484c-ad74-4bbc797d7243
Bykhovskaya, Anna and Phillips, Peter
(2020)
Point optimal testing with roots that are functionally local to unity.
Journal of Econometrics, 219 (2), .
(doi:10.1016/j.jeconom.2020.03.003).
Abstract
Limit theory for regressions involving local to unit roots (LURs) is now used extensively in time series econometric work, establishing power properties for unit root and cointegration tests, assisting the construction of uniform confidence intervals for autoregressive coefficients, and enabling the development of methods robust to departures from unit roots. The present paper shows how to generalize LUR asymptotics to cases where the localized departure from unity is a time varying function rather than a constant. Such a functional local unit root (FLUR) model has much greater generality and encompasses many cases of additional interest that appear in practical work, including structural break formulations that admit subperiods of unit root, local stationary and local explosive behavior within a given sample. Point optimal FLUR tests are constructed in the paper to accommodate such cases and demonstrate how the power envelope changes in situations of practical interest. Against FLUR alternatives, conventional constant point optimal tests can be asymptotically infinitely deficient in power, with poor finite sample power perfor- mance particularly when the departure from unity occurs early in the sample period. New analytic explanation for this phenomenon is provided. Simulation results are reported and some implications for empirical practice are examined
Text
FLUR_JoE_A16
- Accepted Manuscript
More information
Accepted/In Press date: 22 May 2018
e-pub ahead of print date: 28 March 2020
Published date: December 2020
Additional Information:
Funding Information:
Phillips acknowledges support from the National Science Foundation (NSF) under Grant No. SES 12-58258 and from the Korean Government under NRF-2014S1A2A2027803.
Publisher Copyright:
© 2020 Elsevier B.V.
Keywords:
Functional local unit root, Local to unity, Uniform confidence interval, Unit root model
Identifiers
Local EPrints ID: 421897
URI: http://eprints.soton.ac.uk/id/eprint/421897
ISSN: 0304-4076
PURE UUID: cb17c0d7-155d-42f1-b581-68ceb67edcc1
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Date deposited: 06 Jul 2018 16:30
Last modified: 16 Mar 2024 06:46
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Author:
Anna Bykhovskaya
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