Estimation and inference in multivariate Markov chains
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.5/27520 |
Resumo: | The literature of Markov chains has recently focused on modeling multiple categorical data sequences. The usual procedure for handling these multivariate Markov chains (MMC), with m categorical data and s states, consists of expanding the state space by considering ms new states. This model rapidly becomes intractable even with moderate values of m and s due to the excessive number of parameters to estimate. Ching and Fung (2002) found a way to cope with the intractability of the conventional MMC. They also suggested a method of estimation that proved to be inefficient. Zhu and Ching (2010) proposed another method of estimation based on minimizing the prediction error with equality and inequality restrictions. However, both these procedures treat the estimation problem as a mechanic method, without addressing the statistical inference problem. In this article we try to overcome this shortcoming and, at the same time, we propose a new approach to estimate MMC (under Ching et al. hypothesis) which avoids imposing equality and inequality restrictions on the parameters. We illustrate the model and the estimation method with two applications on financial time series data. |
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Estimation and inference in multivariate Markov chainsMultivariate Markov ChainsNonlinear Least SquaresPredictability of Investment RecommendationsStatistical InferenceThe literature of Markov chains has recently focused on modeling multiple categorical data sequences. The usual procedure for handling these multivariate Markov chains (MMC), with m categorical data and s states, consists of expanding the state space by considering ms new states. This model rapidly becomes intractable even with moderate values of m and s due to the excessive number of parameters to estimate. Ching and Fung (2002) found a way to cope with the intractability of the conventional MMC. They also suggested a method of estimation that proved to be inefficient. Zhu and Ching (2010) proposed another method of estimation based on minimizing the prediction error with equality and inequality restrictions. However, both these procedures treat the estimation problem as a mechanic method, without addressing the statistical inference problem. In this article we try to overcome this shortcoming and, at the same time, we propose a new approach to estimate MMC (under Ching et al. hypothesis) which avoids imposing equality and inequality restrictions on the parameters. We illustrate the model and the estimation method with two applications on financial time series data.SpringerRepositório da Universidade de LisboaNicolau, JoãoRiedlinger, Flavio Ivo2023-03-28T12:59:58Z20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/27520engNicolau, João and Flavio Ivo Riedlinger .(2015). “Estimation and inference in multivariate Markov chains”. Statistical Papers, Vol. 56: pp. 1163-1173.10.1007/s00362-014-0630-6info: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:RCAAP2023-04-02T01:34:58Zoai:www.repository.utl.pt:10400.5/27520Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:48:22.338717Repositório 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 |
Estimation and inference in multivariate Markov chains |
title |
Estimation and inference in multivariate Markov chains |
spellingShingle |
Estimation and inference in multivariate Markov chains Nicolau, João Multivariate Markov Chains Nonlinear Least Squares Predictability of Investment Recommendations Statistical Inference |
title_short |
Estimation and inference in multivariate Markov chains |
title_full |
Estimation and inference in multivariate Markov chains |
title_fullStr |
Estimation and inference in multivariate Markov chains |
title_full_unstemmed |
Estimation and inference in multivariate Markov chains |
title_sort |
Estimation and inference in multivariate Markov chains |
author |
Nicolau, João |
author_facet |
Nicolau, João Riedlinger, Flavio Ivo |
author_role |
author |
author2 |
Riedlinger, Flavio Ivo |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Nicolau, João Riedlinger, Flavio Ivo |
dc.subject.por.fl_str_mv |
Multivariate Markov Chains Nonlinear Least Squares Predictability of Investment Recommendations Statistical Inference |
topic |
Multivariate Markov Chains Nonlinear Least Squares Predictability of Investment Recommendations Statistical Inference |
description |
The literature of Markov chains has recently focused on modeling multiple categorical data sequences. The usual procedure for handling these multivariate Markov chains (MMC), with m categorical data and s states, consists of expanding the state space by considering ms new states. This model rapidly becomes intractable even with moderate values of m and s due to the excessive number of parameters to estimate. Ching and Fung (2002) found a way to cope with the intractability of the conventional MMC. They also suggested a method of estimation that proved to be inefficient. Zhu and Ching (2010) proposed another method of estimation based on minimizing the prediction error with equality and inequality restrictions. However, both these procedures treat the estimation problem as a mechanic method, without addressing the statistical inference problem. In this article we try to overcome this shortcoming and, at the same time, we propose a new approach to estimate MMC (under Ching et al. hypothesis) which avoids imposing equality and inequality restrictions on the parameters. We illustrate the model and the estimation method with two applications on financial time series data. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015 2015-01-01T00:00:00Z 2023-03-28T12:59:58Z |
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/10400.5/27520 |
url |
http://hdl.handle.net/10400.5/27520 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Nicolau, João and Flavio Ivo Riedlinger .(2015). “Estimation and inference in multivariate Markov chains”. Statistical Papers, Vol. 56: pp. 1163-1173. 10.1007/s00362-014-0630-6 |
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.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
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 |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131566782283776 |