Transformed regression-based long-horizon predictability tests

Detalhes bibliográficos
Autor(a) principal: Demetrescu, Matei
Data de Publicação: 2023
Outros Autores: Rodrigues, Paulo M.M., Taylor, A. M.Robert
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/161420
Resumo: Funding Information: The authors thank two anonymous referees, the Co-Editor (Torben Andersen), and Tassos Magdalinos for their helpful and constructive feedback on earlier versions of this paper. Rodrigues gratefully acknowledges financial support from the Portuguese Science Foundation (FCT) through project PTDC/EGE-ECO/28924/2017, and (UID/ECO/00124/2013 and Social Sciences DataLab, Project 22209), POR Lisboa (LISBOA-01-0145-FEDER-007722 and Social Sciences DataLab, Project 22209) and POR Norte (Social Sciences DataLab, Project 22209). Taylor gratefully acknowledges financial support provided by the Economic and Social Research Council of the United Kingdom under research grant ES/R00496X/1 . Publisher Copyright: © 2022 The Author(s)
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spelling Transformed regression-based long-horizon predictability tests(Un)conditional heteroskedasticityEndogeneityIVX estimationLong-horizon predictive regressionResidual augmentationUnknown regressor persistenceEconomics and EconometricsFunding Information: The authors thank two anonymous referees, the Co-Editor (Torben Andersen), and Tassos Magdalinos for their helpful and constructive feedback on earlier versions of this paper. Rodrigues gratefully acknowledges financial support from the Portuguese Science Foundation (FCT) through project PTDC/EGE-ECO/28924/2017, and (UID/ECO/00124/2013 and Social Sciences DataLab, Project 22209), POR Lisboa (LISBOA-01-0145-FEDER-007722 and Social Sciences DataLab, Project 22209) and POR Norte (Social Sciences DataLab, Project 22209). Taylor gratefully acknowledges financial support provided by the Economic and Social Research Council of the United Kingdom under research grant ES/R00496X/1 . Publisher Copyright: © 2022 The Author(s)We propose new tests for long-horizon predictability based on IVX estimation of a transformed regression which explicitly accounts for the over-lapping nature of the dependent variable in the long-horizon regression arising from temporal aggregation. To improve efficiency, we moreover incorporate the residual augmentation approach recently used in the context of short-horizon predictability testing by Demetrescu and Rodrigues (2022). Our proposed tests improve on extant tests in the literature in a number of ways. First, they allow practitioners to remain ambivalent over the strength of the persistence of the predictors. Second, they are valid under much weaker conditions on the innovations than extant long-horizon predictability tests; in particular, we allow for general forms of conditional and unconditional heteroskedasticity in the innovations, neither of which are tied to a parametric model. Third, unlike the popular Bonferroni-based methods in the literature, our proposed tests can handle multiple predictors, and can be easily implemented as either one or two-sided hypotheses tests. Monte Carlo analysis suggests that our preferred tests offer improved finite sample properties compared to the leading tests in the literature. We report results from an empirical application investigating the use of real exchange rates for predicting nominal exchange rates and inflation.NOVA School of Business and Economics (NOVA SBE)RUNDemetrescu, MateiRodrigues, Paulo M.M.Taylor, A. M.Robert2023-12-18T22:26:33Z2023-122023-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/161420eng0304-4076PURE: 46087478https://doi.org/10.1016/j.jeconom.2022.06.006info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:44:18Zoai:run.unl.pt:10362/161420Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:58:31.043883Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Transformed regression-based long-horizon predictability tests
title Transformed regression-based long-horizon predictability tests
spellingShingle Transformed regression-based long-horizon predictability tests
Demetrescu, Matei
(Un)conditional heteroskedasticity
Endogeneity
IVX estimation
Long-horizon predictive regression
Residual augmentation
Unknown regressor persistence
Economics and Econometrics
title_short Transformed regression-based long-horizon predictability tests
title_full Transformed regression-based long-horizon predictability tests
title_fullStr Transformed regression-based long-horizon predictability tests
title_full_unstemmed Transformed regression-based long-horizon predictability tests
title_sort Transformed regression-based long-horizon predictability tests
author Demetrescu, Matei
author_facet Demetrescu, Matei
Rodrigues, Paulo M.M.
Taylor, A. M.Robert
author_role author
author2 Rodrigues, Paulo M.M.
Taylor, A. M.Robert
author2_role author
author
dc.contributor.none.fl_str_mv NOVA School of Business and Economics (NOVA SBE)
RUN
dc.contributor.author.fl_str_mv Demetrescu, Matei
Rodrigues, Paulo M.M.
Taylor, A. M.Robert
dc.subject.por.fl_str_mv (Un)conditional heteroskedasticity
Endogeneity
IVX estimation
Long-horizon predictive regression
Residual augmentation
Unknown regressor persistence
Economics and Econometrics
topic (Un)conditional heteroskedasticity
Endogeneity
IVX estimation
Long-horizon predictive regression
Residual augmentation
Unknown regressor persistence
Economics and Econometrics
description Funding Information: The authors thank two anonymous referees, the Co-Editor (Torben Andersen), and Tassos Magdalinos for their helpful and constructive feedback on earlier versions of this paper. Rodrigues gratefully acknowledges financial support from the Portuguese Science Foundation (FCT) through project PTDC/EGE-ECO/28924/2017, and (UID/ECO/00124/2013 and Social Sciences DataLab, Project 22209), POR Lisboa (LISBOA-01-0145-FEDER-007722 and Social Sciences DataLab, Project 22209) and POR Norte (Social Sciences DataLab, Project 22209). Taylor gratefully acknowledges financial support provided by the Economic and Social Research Council of the United Kingdom under research grant ES/R00496X/1 . Publisher Copyright: © 2022 The Author(s)
publishDate 2023
dc.date.none.fl_str_mv 2023-12-18T22:26:33Z
2023-12
2023-12-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/161420
url http://hdl.handle.net/10362/161420
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0304-4076
PURE: 46087478
https://doi.org/10.1016/j.jeconom.2022.06.006
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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