This can be used to take into account possible heteroscedasticity.
Engle, R. F. (1982), Autoregressive conditionally heteroscedasticity with estimates of the variance of United Kingdom inflation, Econmetrica 50, 987-1007.
Engle, R.F. (1982), “Autoregressive Conditional Heteroscedasticity With Estimates of the Variance of the United Kingdom Inflation,” Econometrica 50, 377–403.
Engle R (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica 50: 987–1007.
Engle, R. F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, 50, 987–1007.
in the context of the generalized autoregressive conditional heteroskedastic (GARCH)
general undertaking generalized autoregressive conditional heteroskedasticity (GARCH)
Bollerslev, T. (1986): “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics, 31, 307–327.
Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31, 307-327.
Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31, 307–327.
Engle, R.F. (1982) “Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation,” Econometrica 50(4), 987–1007.
Engle, R.F. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of theVariance of United Kingdom Inflation,” Econometrica , 50, 987-1007.
R. Engle., Autoregressive Conditional Heteroskedasticity with Estimates of Variance of United Kingdom Inflation, Econometrica 50, (987-1008) 1982
(m) Engle, R., 1982, “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation,” Econometrica 50, 987-1008.
Engle, R. F. [1982] “Autoregressive conditional heteroscedastic models with estimates of the variance of United Kingdom inflation”, Econometrica 50: 987-1007.
The model has no heteroskedasticity (meaning the variance of the error is the same regardless of the independent variable’s value).
If the data (as indicated by Levene’s test) are heteroscedastic (i.e., not homogeneous), then the graphs of the residuals should be examined.
If it is suspected that the variances are not homogeneous (a representation of the residuals against the explanatory variables may reveal heteroscedasticity), it is therefore necessary to perform a test for heteroscedasticity.
Requêtes fréquentes français :1-200, -1k, -2k, -3k, -4k, -5k, -7k, -10k, -20k, -40k, -100k, -200k, -500k, -1000k,
Requêtes fréquentes anglais :1-200, -1k, -2k, -3k, -4k, -5k, -7k, -10k, -20k, -40k, -100k, -200k, -500k, -1000k,
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