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Uncovering regimes in out of sample forecast errors

Uncovering regimes in out of sample forecast errors
Uncovering regimes in out of sample forecast errors
We introduce a set of test statistics for assessing the presence of regimes in out of sample forecast errors produced by recursively estimated linear multiple predictive regressions. These predictive regressions can accommodate multiple predictors that are highly persistent with potentially different degrees of persistence. Our method is also designed to be robust to the chosen starting window size so as to avert data mining concerns. Our tests are shown to be consistent and to lead to null distributions that are free of nuisance parameters and hence robust to the degree of persistence of the predictors
1803
1-21
University of Southampton
Emiliano Da Silva Neto, Anibal
11efc93a-9f4f-4fe6-bee2-0297b575115b
Gonzalo, Jesus
57637a0a-f7da-417f-9d2e-3a33a7082504
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
Emiliano Da Silva Neto, Anibal
11efc93a-9f4f-4fe6-bee2-0297b575115b
Gonzalo, Jesus
57637a0a-f7da-417f-9d2e-3a33a7082504
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51

Emiliano Da Silva Neto, Anibal, Gonzalo, Jesus and Pitarakis, Jean-Yves (2018) Uncovering regimes in out of sample forecast errors (Discussion Papers in Economics and Econometrics, 1803) University of Southampton 21pp.

Record type: Monograph (Working Paper)

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 multiple predictive regressions. These predictive regressions can accommodate multiple predictors that are highly persistent with potentially different degrees of persistence. Our method is also designed to be robust to the chosen starting window size so as to avert data mining concerns. Our tests are shown to be consistent and to lead to null distributions that are free of nuisance parameters and hence robust to the degree of persistence of the predictors

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Published date: 16 April 2018

Identifiers

Local EPrints ID: 419830
URI: http://eprints.soton.ac.uk/id/eprint/419830
PURE UUID: 6bd48a39-a363-4fa0-a4af-fd24ab74b1d3
ORCID for Jean-Yves Pitarakis: ORCID iD orcid.org/0000-0002-6305-7421

Catalogue record

Date deposited: 23 Apr 2018 16:30
Last modified: 22 Nov 2021 02:49

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Contributors

Author: Anibal Emiliano Da Silva Neto
Author: Jesus Gonzalo

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