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Registro Completo |
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
09/06/1997 |
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Data da última atualização: |
12/12/2007 |
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Autoria: |
VIEIRA, L. F. |
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Título: |
Forecasting with multivariate time series models; an application to the sea scallop fishery. |
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Ano de publicação: |
1987 |
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Fonte/Imprenta: |
1987. |
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Páginas: |
348p. |
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Idioma: |
Inglês |
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Notas: |
Tese (Doutorado) - University of Rhode Island, Rhode Island. |
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Conteúdo: |
The objective of this research is to develop time series forecasting models for economic variables characterizing the U.S. Atlantic sea scallop fishery, namely, ex-vessel prices, effort and landings. The behavior of these variables, especially landings, is highly dependent on the stock behavior of the fisheries. This component of the system, however, has shown wide variations over time, without any apparent recurrent pattern. In this situation, univariate models may lead to imprecise forecasts and/or display non-stable behavior. A vector model, incorporating the feedback relationships between the variables of the system, usually leads to more stable representations and more accurate forecasts. Identification of these vector stochastic time series models, inthe sense of choosing and adequate representation within a family or between families of admissible representations, is a complex problem for which definitive solutions are not yet available. In this research, the identification problem is approached by methods suggested by Pandit (1973) and Akaike (1974, 1976), as alternatives to the better known Box-Jenkins approach or its extensions to the vector process problem. These methods, unlike box-Jenkins rely on objective criteria to choose between alternative models. Pandit's approach is based on results stating that stationary stochastic process can be arbitrarily closely approximated by ARMAV (n, n-1) models. Akaike's approach is based on the interpretation of the maximum likelihood as an... MenosThe objective of this research is to develop time series forecasting models for economic variables characterizing the U.S. Atlantic sea scallop fishery, namely, ex-vessel prices, effort and landings. The behavior of these variables, especially landings, is highly dependent on the stock behavior of the fisheries. This component of the system, however, has shown wide variations over time, without any apparent recurrent pattern. In this situation, univariate models may lead to imprecise forecasts and/or display non-stable behavior. A vector model, incorporating the feedback relationships between the variables of the system, usually leads to more stable representations and more accurate forecasts. Identification of these vector stochastic time series models, inthe sense of choosing and adequate representation within a family or between families of admissible representations, is a complex problem for which definitive solutions are not yet available. In this research, the identification problem is approached by methods suggested by Pandit (1973) and Akaike (1974, 1976), as alternatives to the better known Box-Jenkins approach or its extensions to the vector process problem. These methods, unlike box-Jenkins rely on objective criteria to choose between alternative models. Pandit's approach is based on results stating that stationary stochastic process can be arbitrarily closely approximated by ARMAV (n, n-1) models. Akaike's approach is based on the interpretation of the maximum li... Mostrar Tudo |
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Palavras-Chave: |
Economy; Fishing; Model; Modelo; Previsão. |
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Thesagro: |
Economia; Pesca. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02090nam a2200217 a 4500
001 1090787
005 2007-12-12
008 1987 bl uuuu m 00u1 u #d
100 1 $aVIEIRA, L. F.
245 $aForecasting with multivariate time series models; an application to the sea scallop fishery.
260 $a1987.$c1987
300 $a348p.
500 $aTese (Doutorado) - University of Rhode Island, Rhode Island.
520 $aThe objective of this research is to develop time series forecasting models for economic variables characterizing the U.S. Atlantic sea scallop fishery, namely, ex-vessel prices, effort and landings. The behavior of these variables, especially landings, is highly dependent on the stock behavior of the fisheries. This component of the system, however, has shown wide variations over time, without any apparent recurrent pattern. In this situation, univariate models may lead to imprecise forecasts and/or display non-stable behavior. A vector model, incorporating the feedback relationships between the variables of the system, usually leads to more stable representations and more accurate forecasts. Identification of these vector stochastic time series models, inthe sense of choosing and adequate representation within a family or between families of admissible representations, is a complex problem for which definitive solutions are not yet available. In this research, the identification problem is approached by methods suggested by Pandit (1973) and Akaike (1974, 1976), as alternatives to the better known Box-Jenkins approach or its extensions to the vector process problem. These methods, unlike box-Jenkins rely on objective criteria to choose between alternative models. Pandit's approach is based on results stating that stationary stochastic process can be arbitrarily closely approximated by ARMAV (n, n-1) models. Akaike's approach is based on the interpretation of the maximum likelihood as an...
650 $aEconomia
650 $aPesca
653 $aEconomy
653 $aFishing
653 $aModel
653 $aModelo
653 $aPrevisão
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Registro original: |
Embrapa Unidades Centrais (AI-SEDE) |
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Registro Completo
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Biblioteca(s): |
Embrapa Recursos Genéticos e Biotecnologia. |
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Data corrente: |
30/01/2017 |
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Data da última atualização: |
28/03/2023 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
GRYNBERG, P.; GUIMARÃES, L. A.; COSTA, M. M. C.; TOGAWA, R. C.; BRASILEIRO, A. C. M.; GUIMARAES, P. M. |
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Afiliação: |
PRISCILA GRYNBERG, Cenargen; LARISSA A. GUIMARÃES, colaborador, CENARGEN; MARCOS MOTA DO CARMO COSTA, Cenargen; ROBERTO COITI TOGAWA, Cenargen; ANA CRISTINA MIRANDA BRASILEIRO, Cenargen; PATRICIA MESSEMBERG GUIMARAES, Cenargen. |
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Título: |
Comprehensive profiling and characterization of Arachis stenosperma (peanut) and Meloidogyne arenaria (plantroot nematode) smallRNAs identified during the course of the infection. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE OF THE AB3C, 12., 2016, Belo Horizonte. [Proceedings...] [S.l.]: AB3C, 2016. |
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Idioma: |
Inglês |
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Notas: |
X-meeting 2016. |
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Thesaurus NAL: |
Meloidogyne. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/154215/1/Resumo-Xmeeting-PriscilaGrynberg.pdf
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Marc: |
LEADER 00717nam a2200181 a 4500
001 2062094
005 2023-03-28
008 2016 bl uuuu u00u1 u #d
100 1 $aGRYNBERG, P.
245 $aComprehensive profiling and characterization of Arachis stenosperma (peanut) and Meloidogyne arenaria (plantroot nematode) smallRNAs identified during the course of the infection.$h[electronic resource]
260 $aIn: INTERNATIONAL CONFERENCE OF THE AB3C, 12., 2016, Belo Horizonte. [Proceedings...] [S.l.]: AB3C$c2016
500 $aX-meeting 2016.
650 $aMeloidogyne
700 1 $aGUIMARÃES, L. A.
700 1 $aCOSTA, M. M. C.
700 1 $aTOGAWA, R. C.
700 1 $aBRASILEIRO, A. C. M.
700 1 $aGUIMARAES, P. M.
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Embrapa Recursos Genéticos e Biotecnologia (CENARGEN) |
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