Note: This page has been translated by MathWorks. Click here to see

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

**Class: **regARIMA

Infer innovations of regression models with ARIMA errors

`E = infer(Mdl,Y)`

[E,U,V,logL]
= infer(Mdl,Y)

[E,U,V,logL]
= infer(Mdl,Y,Name,Value)

infers
residuals of a univariate regression model with ARIMA time series
errors fit to response data `E`

= infer(`Mdl`

,`Y`

)`Y`

.

`[`

additionally
returns the unconditional disturbances `E`

,`U`

,`V`

,`logL`

]
= infer(`Mdl`

,`Y`

)`U`

, the innovation
variances `V`

, and the loglikelihood objective function
values `logL`

.

`[`

returns
the output arguments using additional options specified by one or
more `E`

,`U`

,`V`

,`logL`

]
= infer(`Mdl`

,`Y`

,`Name,Value`

)`Name,Value`

pair arguments.

[1] 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.

[2] Davidson, R., and J. G. MacKinnon. *Econometric
Theory and Methods*. Oxford, UK: Oxford University Press,
2004.

[3] Enders, W. *Applied Econometric Time Series*.
Hoboken, NJ: John Wiley & Sons, Inc., 1995.

[4] Hamilton, J. D. *Time Series Analysis*.
Princeton, NJ: Princeton University Press, 1994.

[5] Pankratz, A. *Forecasting with Dynamic Regression
Models.* John Wiley & Sons, Inc., 1991.

[6] Tsay, R. S. *Analysis of Financial Time Series*.
2nd ed. Hoboken, NJ: John Wiley & Sons, Inc., 2005.