forecast |

Computes (n-period ahead) dynamic forecasts of the VAR or VEC equation.

forecast computes the forecast for all variables and all observations in a specified sample. In some settings, you may instruct forecast to compare the forecasted data to actual data, and to compute summary statistics.

Syntax

var_name.forecast(options) f_pattern [se_pattern]

You should enter a naming suffix for the forecast series and, optionally, a naming suffix for the series containing the standard errors. Forecast standard errors are currently only available for non-Bayesian VARs, and are computed via simulation.

Not currently available for switching VARs

Options

General Options

g | Graph the forecasts in individual graphs - one per dependent variable. |

m | Graph the forecasts in a combined graph. |

e | Produce the forecast evaluation table. |

f = arg (default= “actual”) | Out-of-forecast-sample fill behavior: “actual” (fill observations outside the forecast sample with actual values for the fitted variable), “na” (fill observations outside the forecast sample with missing values). |

prompt | Force the dialog to appear from within a program. |

p | Print view. |

Non-Bayesian Options

streps=integer | Number of simulation repetitions. Only applicable if a se_pattern is provided. |

f=number | Fraction of failed repetitions before stopping. Only applicable if a se_pattern is provided. |

BVAR Options

classical | Perform classical forecasting – forecast based upon the posterior means of the coefficients as if they were calculated from a classical VAR. If omitted Bayesian sampling is used. |

If “classical” is not specified, the following Bayesian forecasting options are available:

mean | Store the mean of the draws from the sampler. If omitted the median is stored. |

draws=integer (default= 100000) | Number of draws. |

burn=arg (default=0.1) | Proportion of initial draws to discard. |

seed=integer | Random number seed. |

dropunstable | Drop any draws that produce unstable coefficients. |

dgraph | Produce distribution graphs. |

fangraph | Produce fan graphs. |

page=arg | Store the individual draws in a new page. |

BTVCVAR Options

usemean | Use posterior mean as the point estimate. The posterior median is used if usemean is not included in the options list. |

showci | Show credibility intervals (bands). |

cilevels = arg (default = "0.95") | Set credibility levels. For multiple levels, enter a space-delimited list of values surrounded by quotation marks, e.g., "0.3 0.5 0.8". |

uselines | Use lines instead of shading for credibility intervals. |

seed = int | Set the random seed. EViews will generate a seed if one is not specified. |

rng = arg (default = “kn” or method set via rndseed) | Set random number generator type. Available types are: improved Knuth generator (“kn”), improved Mersenne Twister (“mt”), Knuth’s (1997) lagged Fibonacci generator used in EViews 4 (“kn4”), L’Ecuyer’s (1999) combined multiple recursive generator (“le”), Matsumoto and Nishimura’s (1998) Mersenne Twister used in EViews 4 (“mt4”). |

Examples

The following lines:

smpl 1970q1 1990q4

var var1.ls 1 3 con inc

smpl 1991q1 1995q4

var1.forecast(m) _f _se

estimate a VAR over the period 1970Q1–1990Q4, and then computes dynamic forecasts for the period 1991Q1–1995Q4, and plots the forecasts as line graphs.

smpl 1970q1 1990q4

var var2.bvar(prior=inw) 1 3 con inc

smpl 1991q1 1995q4

var1.forecast(m, draws=50000, burn=.05, dgraph, page=draws) _f

estimates a Bayesian VAR with an independent normal-Wishart prior over the same period, and then forecasts that VAR taking 50,000 draws of a Gibbs sampler, discarding the first 2,500 draws, producing a distribution graph of the forecasts and storing the draws into a new panel page called DRAWS.

Cross-references

See
“Forecasting” for a discussion of forecasting from VARs and VECS.

See also
Var::fit.