Uncovering regimes in out of sample forecast errors from predictive regressions*
Uncovering regimes in out of sample forecast errors from predictive regressions*
We introduce a set of test statistics for assessing the presence of regimes in out of sample forecast errors produced by recursively estimated linear predictive regressions that can accommodate multiple highly persistent predictors. Our test statistics are designed to be robust to the chosen starting window size and are shown to be both consistent and locally powerful. Their limiting null distributions are also free of nuisance parameters and hence robust to the degree of persistence of the predictors. Our methods are subsequently applied to the predictability of the value premium whose dynamics are shown to be characterized by state dependence.
predictive regressions, Thresholds, CUSUM, Forecast errors
713-741
Emiliano Da Silva Neto, Anibal
11efc93a-9f4f-4fe6-bee2-0297b575115b
Gonzalo, Jesús
48015f9d-eef0-4ebd-8f2b-cbe7aa0cf667
Pitarakis, Jean Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
June 2021
Emiliano Da Silva Neto, Anibal
11efc93a-9f4f-4fe6-bee2-0297b575115b
Gonzalo, Jesús
48015f9d-eef0-4ebd-8f2b-cbe7aa0cf667
Pitarakis, Jean Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
Emiliano Da Silva Neto, Anibal, Gonzalo, Jesús and Pitarakis, Jean Yves
(2021)
Uncovering regimes in out of sample forecast errors from predictive regressions*.
Oxford Bulletin of Economics and Statistics, 83 (3), .
(doi:10.1111/obes.12418).
Abstract
We introduce a set of test statistics for assessing the presence of regimes in out of sample forecast errors produced by recursively estimated linear predictive regressions that can accommodate multiple highly persistent predictors. Our test statistics are designed to be robust to the chosen starting window size and are shown to be both consistent and locally powerful. Their limiting null distributions are also free of nuisance parameters and hence robust to the degree of persistence of the predictors. Our methods are subsequently applied to the predictability of the value premium whose dynamics are shown to be characterized by state dependence.
Text
OBES-EmilianoGonzaloPitarakis
- Accepted Manuscript
Text
Gonzalo-Pitarakis-OBES-Accepted-version
- Accepted Manuscript
More information
Accepted/In Press date: 23 September 2020
e-pub ahead of print date: 13 January 2021
Published date: June 2021
Keywords:
predictive regressions, Thresholds, CUSUM, Forecast errors
Identifiers
Local EPrints ID: 444124
URI: http://eprints.soton.ac.uk/id/eprint/444124
ISSN: 0305-9049
PURE UUID: 56d17bae-9f36-452f-ae06-f71c08d0f66a
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Date deposited: 28 Sep 2020 16:30
Last modified: 17 Mar 2024 05:56
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Contributors
Author:
Anibal Emiliano Da Silva Neto
Author:
Jesús Gonzalo
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