Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | http://hdl.handle.net/10438/11806 |
Resumo: | This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered. |
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Guillen, Osmani Teixeira CarvalhoHecq, AlainIssler, João VictorSaraiva, Diogo Vinícius MenezesEscolas::EPGEFGV2014-06-02T13:21:28Z2014-06-02T13:21:28Z2014-06-020104-8910http://hdl.handle.net/10438/11806This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.engFundação Getulio Vargas. Escola de Pós-graduação em EconomiaEnsaios Econômicos;753ForecastingMultivariate modelsVector autoregression (VAR)Present-value restrictionsCommon cyclesCointegrationInterest ratesPrices and dividendsEconomiaEconomiaCointegraçãoTaxas de jurosForecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictionsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlereponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessLICENSElicense.txtlicense.txttext/plain; charset=utf-84707https://repositorio.fgv.br/bitstreams/ce70fa2f-7d84-4f55-883e-9f9ba68588dc/downloaddfb340242cced38a6cca06c627998fa1MD52ORIGINALForecasting-Multivariate-Time-Series-under-Present-Value-Model-Short--and-Long-run-Co-movement-Restrictions.pdfForecasting-Multivariate-Time-Series-under-Present-Value-Model-Short--and-Long-run-Co-movement-Restrictions.pdfMain 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dc.title.eng.fl_str_mv |
Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions |
title |
Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions |
spellingShingle |
Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions Guillen, Osmani Teixeira Carvalho Forecasting Multivariate models Vector autoregression (VAR) Present-value restrictions Common cycles Cointegration Interest rates Prices and dividends Economia Economia Cointegração Taxas de juros |
title_short |
Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions |
title_full |
Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions |
title_fullStr |
Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions |
title_full_unstemmed |
Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions |
title_sort |
Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions |
author |
Guillen, Osmani Teixeira Carvalho |
author_facet |
Guillen, Osmani Teixeira Carvalho Hecq, Alain Issler, João Victor Saraiva, Diogo Vinícius Menezes |
author_role |
author |
author2 |
Hecq, Alain Issler, João Victor Saraiva, Diogo Vinícius Menezes |
author2_role |
author author author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EPGE |
dc.contributor.affiliation.none.fl_str_mv |
FGV |
dc.contributor.author.fl_str_mv |
Guillen, Osmani Teixeira Carvalho Hecq, Alain Issler, João Victor Saraiva, Diogo Vinícius Menezes |
dc.subject.eng.fl_str_mv |
Forecasting Multivariate models Vector autoregression (VAR) Present-value restrictions Common cycles Cointegration Interest rates Prices and dividends |
topic |
Forecasting Multivariate models Vector autoregression (VAR) Present-value restrictions Common cycles Cointegration Interest rates Prices and dividends Economia Economia Cointegração Taxas de juros |
dc.subject.area.por.fl_str_mv |
Economia |
dc.subject.bibliodata.por.fl_str_mv |
Economia Cointegração Taxas de juros |
description |
This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered. |
publishDate |
2014 |
dc.date.accessioned.fl_str_mv |
2014-06-02T13:21:28Z |
dc.date.available.fl_str_mv |
2014-06-02T13:21:28Z |
dc.date.issued.fl_str_mv |
2014-06-02 |
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/10438/11806 |
dc.identifier.issn.none.fl_str_mv |
0104-8910 |
identifier_str_mv |
0104-8910 |
url |
http://hdl.handle.net/10438/11806 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofseries.por.fl_str_mv |
Ensaios Econômicos;753 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Fundação Getulio Vargas. Escola de Pós-graduação em Economia |
publisher.none.fl_str_mv |
Fundação Getulio Vargas. Escola de Pós-graduação em Economia |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional do FGV (FGV Repositório Digital) instname:Fundação Getulio Vargas (FGV) instacron:FGV |
instname_str |
Fundação Getulio Vargas (FGV) |
instacron_str |
FGV |
institution |
FGV |
reponame_str |
Repositório Institucional do FGV (FGV Repositório Digital) |
collection |
Repositório Institucional do FGV (FGV Repositório Digital) |
bitstream.url.fl_str_mv |
https://repositorio.fgv.br/bitstreams/ce70fa2f-7d84-4f55-883e-9f9ba68588dc/download https://repositorio.fgv.br/bitstreams/58da4f66-cc4e-4607-8caa-fec9bf48da14/download https://repositorio.fgv.br/bitstreams/54f45295-e76e-4598-af0f-b5de5decf937/download https://repositorio.fgv.br/bitstreams/90f6b8c8-1c32-42ef-8d3d-b90e7ca4cc50/download |
bitstream.checksum.fl_str_mv |
dfb340242cced38a6cca06c627998fa1 af122f7a8e763746e86ad37cbb263196 f91420e9ec35522ee223446fdac758c8 7a7d47d7f091103b8b64dd65905a5008 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas (FGV) |
repository.mail.fl_str_mv |
|
_version_ |
1802749866577756160 |