Usage Arguments Details Value Author(s) References See Also Examples
View source: R/CMISMultiForecastExtraFunctions.R
1 | Mresid(forecast)
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forecast |
Objeto forecast |
S<c3><a3>o efetuados seis testes nos res<c3><ad>duos sendo eles: teste de independ<c3><aa>ncia de Box-Pierce e Ljung-Box, teste de nulidade da m<c3><a9>dia de t-Student, teste de ruido branco de Portmanteau, teste de normalidade de Jarque Bera, teste de heterocedasticidade de Breusch-Pagan e por fim, teste de autocorrela<c3><a7><c3><a3>o de Durbin-Watson. Um indicador de qualidade do res<c3><ad>duo <c3><a9> gerado ao final com pesos maiores para independ<c3><aa>ncia, heterocedasticidade e autocorrela<c3><a7><c3><a3>o dos res<c3><ad>duos.
Resultados dos testes.
LOPES, J. E.
Box, G. E. P. and Pierce, D. A. (1970), Distribution of residual correlations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association, 65, 1509-1526.
Ljung, G. M. and Box, G. E. P. (1978), On a measure of lack of fit in time series models. Biometrika 65, 297-303.
Harvey, A. C. (1993) Time Series Models. 2nd Edition, Harvester Wheatsheaf, NY, pp. 44, 45.
J. B. Cromwell, W. C. Labys and M. Terraza (1994): Univariate Tests for Time Series Models, Sage, Thousand Oaks, CA, pages 20-22.
T.S. Breusch & A.R. Pagan (1979), A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica 47, 1287-1294
J. Durbin & G.S. Watson (1971), Testing for Serial Correlation in Least Squares Regression III. Biometrika 58, 1-19.
J. Racine & R. Hyndman (2002), Using R To Teach Econometrics. Journal of Applied Econometrics 17, 175-189.
1 2 3 4 5 6 | ## Not run
## dados
#data(diario)
#y <- ConvertDataToTs(diario[,1:2], tsfrequency = "day", OutType = "ts")
#fit <- auto.arima(y)
#Mresid(fit)
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