Publication: Testing for first order serial correlation in temporally aggregated regression models
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eng
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BR
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Instituto de Pesquisa Econômica Aplicada (Ipea)
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Discussion Paper 14 : Testing for first order serial correlation in temporally aggregated regression models
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Abstract
Mostra que a estatística LM para testar correlações seriadas de primeira ordem nos modelos de regressão podem ser computados usando o Filtro de Kalman. Demonstra que, quando faltam observações, a estatística LM para este teste é equivalente à estatística-teste derivada de Robinson (1985) usando a condição de semelhança nos tempos observados. Dá-se preferência ao modelo de Filtro de Kalman porque a estatística-teste para correlações seriadas de primeira ordem em modelos de regressões agregadas temporais podem ser obtidos como uma extensão do caso anterior.
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Shows that the LM statistic for testing serial correlation of first order in the regression models can be computed using the Kalman filter. Shows that when missing observations, LM statistic for this test is equivalent to the Robinson derived statistical test (1985) using the similarity observed in the condition of time. It is preferred that the Kalman filter model for the statistical test for first-order serial correlation in aggregate regression models of time can be obtained as an extension of the previous case.