Class: arima
Forecast ARIMA or ARIMAX process
[Y,YMSE]
= forecast(Mdl,numPeriods)
[Y,YMSE,V]
= forecast(Mdl,numPeriods)
[Y,YMSE,V] = forecast(Mdl,numPeriods,Name,Value)
[
forecasts
responses for a univariate ARIMA model, and generates corresponding
mean square errors, Y
,YMSE
]
= forecast(Mdl
,numPeriods
)YMSE
.
[
additionally
forecasts conditional variances for an ARIMA model with a conditional
variance model.Y
,YMSE
,V
]
= forecast(Mdl
,numPeriods
)
[Y,YMSE,V] = forecast(Mdl,numPeriods,
generates
the forecasts with additional options specified by one or more Name,Value
)Name,Value
pair
arguments.
[1] Baillie, R., and T. Bollerslev. “Prediction in Dynamic Models with Time-Dependent Conditional Variances.” Journal of Econometrics. Vol. 52, 1992, pp. 91–113.
[2] Bollerslev, T. “Generalized Autoregressive Conditional Heteroskedasticity.” Journal of Econometrics. Vol. 31, 1996, pp. 307–327.
[3] Bollerslev, T. “A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return.” The Review Economics and Statistics. Vol. 69, 1987, pp. 542–547.
[4] Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. Time Series Analysis: Forecasting and Control 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1994.
[5] Enders, W. Applied Econometric Time Series. Hoboken, NJ: John Wiley & Sons, 1995.
[6] Engle, R. F. “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation.” Econometrica. Vol. 50, 1982, pp. 987–1007.
[7] Hamilton, J. D. Time Series Analysis. Princeton, NJ: Princeton University Press, 1994.