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Registro Completo |
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Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
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Data corrente: |
26/06/1996 |
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Data da última atualização: |
26/06/1996 |
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Autoria: |
JUDGE, G. G.; HILL, R. C.; GRIFFITHS, W. E.; LUTKEPOHL, H.; LEE, T.-C. |
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Título: |
Sets of linear statistical models. |
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Edição: |
2. ed |
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Ano de publicação: |
1988 |
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Fonte/Imprenta: |
Introduction to the theory and Practice of Econometrics. New York: 1988. |
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Páginas: |
p.443-496 |
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Idioma: |
Inglês |
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Conteúdo: |
In most of the earlier chapters we concentrated on estimation and hypothesis testing for the parameter vector B in the single equation model y=XB + e. Different estimation methods and tests were considered, depeding on what further assumptions were made about the disturbance convariance matrix E[ee]. In this Chapter we turn to a situation where there is more than one equation to estimate. For example, interest might center on demand equations for a number of commodities, investment functions a number of firms, or consumption functions for subsets of the population. Suppose that time-series data suitable for estimation of a number of demand equations are available. The disturbances in these different equations at a given time are likely to reflect some common unmeasurable or omitted factors, and hence could be correlated. The same is also likely to be true when time-series observations are used to estimate different investment functions for different firms, different consumption functions for different subsets of the population, or many other examples. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01562naa a2200193 a 4500
001 1647647
005 1996-06-26
008 1988 bl --- 0-- u #d
100 1 $aJUDGE, G. G.
245 $aSets of linear statistical models.
250 $a2. ed
260 $c1988
300 $ap.443-496
520 $aIn most of the earlier chapters we concentrated on estimation and hypothesis testing for the parameter vector B in the single equation model y=XB + e. Different estimation methods and tests were considered, depeding on what further assumptions were made about the disturbance convariance matrix E[ee]. In this Chapter we turn to a situation where there is more than one equation to estimate. For example, interest might center on demand equations for a number of commodities, investment functions a number of firms, or consumption functions for subsets of the population. Suppose that time-series data suitable for estimation of a number of demand equations are available. The disturbances in these different equations at a given time are likely to reflect some common unmeasurable or omitted factors, and hence could be correlated. The same is also likely to be true when time-series observations are used to estimate different investment functions for different firms, different consumption functions for different subsets of the population, or many other examples.
700 1 $aHILL, R. C.
700 1 $aGRIFFITHS, W. E.
700 1 $aLUTKEPOHL, H.
700 1 $aLEE, T.-C.
773 $tIntroduction to the theory and Practice of Econometrics. New York: 1988.
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Embrapa Mandioca e Fruticultura (CNPMF) |
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