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
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
16 April 2018
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
Text
1803 combined
- Version of Record
Available under License Other.
More information
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
Catalogue record
Date deposited: 23 Apr 2018 16:30
Last modified: 16 Mar 2024 03:32
Export record
Contributors
Author:
Anibal Emiliano Da Silva Neto
Author:
Jesus Gonzalo
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
