-------------------------------------------------------------------------------
help for  dpredict                                     juraj.katriak@iskwien.at
-------------------------------------------------------------------------------

Title
        dpredict -- Dynamic forecasting for ARIMA(p,d,q) models

Syntax

        dpredict [varlist ] , from(#) to(#) periods(#) [lvl] :  command

    options                     Description
    -------------------------------------------------------------------------
      from(#)                   the number of the last observation of the
                                  first subsample
      to(#)                     the number of the last observation of the
                                  last subsample
      periods(#)                horizon (no. of periods) of the dynamic
                                  forecasts
      lvl                       specifies that the dependent variable of the
                                  arima-command is in levels, rather than in
                                  differences
      norecursive               specifies that the subsamples will have a
                                  fixed size, rather than grow
    -------------------------------------------------------------------------
    command must be a valid arima estimation command

Description

    dpredict calculates dynamic predictions from the command, which must be
    the arima command.  The predictions calculated by dpredict are all
    out-of-sample predictions, each based on a different subsample. The
    horizon of the predictions (how many periods ahead) is specified by the
    periods(#) option. The subsamples, which the predictions are based on,
    change sequentially starting with the subsample delimited by 1/from(#) up
    to 1/to(#). If the norecursive option is specified, then the subsamples
    will have a fixed size of from(#) observations, i.e.  the first subsample
    will be 1/from(#) and the last (to(#)-from(#))/to(#).

    If the lvl options is not specified, then the direction-of-change
    statistic is computed assuming that the dependent variable of the
    supplied arima-commmand is in differences corresponding to the forecast
    horizon specified in periods(#) (i.e. if the forecast horizon is e.g. 3
    then the it is assumed that the dependent variable is of the form of
    s3.dep_var).

    If varlist is supplied then in each step the last observation in the
    current subsample of the all variables in specified in arima (including
    the dependent) is replaced by the corresponding observation from varlist.
    Hence, if varlist is supplied, it must contain the same number of
    variables as the arima command. This allows for the possibility that the
    last observation of some variables in the dataset must, in fact, be
    predicted, because it is not available at the time when making the
    forecasts.

Saved Results

    dpredict saves in r(): scalars , r(RMSE), r(MAE) and r(DOC), containing
    the computed statistcs RMSE, MAE, DOC and matrices , r(Yhat) and
    r(ResMat), which contain the out-of-sample predictions and residuals
    respectively. Additionaly a matrix , r(DOCmat), with elements 1 (if the
    direction of change was predicted correctly) and 0 (if not) is saved.

Remarks

    The statistisc computed along with the predictions are: RMSE - root mean
    square error, MAE - mean absolute errror and DOE - direction of change
    (gives the proportion of correctly predicted values of dep_var)

Examples

    . use http://www.stata-press.com/data/r9/wpi1

    . gen wpi_df = d.wpi

    . dpredict , f(80) t(124) p(1) : arima wpi_df , arima(1,0,1)

Author

    J. Katriak, ISK Vienna, juraj.katriak@iskwien.at

Also see

    Online: help for arima, arima_postestimation