forecast |

Computes dynamic forecasts of an estimated equation.

forecast computes the forecast for 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

eq_name.forecast(options) yhat [y_se]

eq_name.forecast(options) yhat [y_se y_var]

Enter a name for the forecast series and, optionally, a name for the series containing the standard errors. For ARCH specifications, you may use the second form of the command, and optionally enter a name for the conditional variance series. Forecast standard errors are currently not available for binary or censored models. forecast is not available for models estimated using ordered methods.

Options

d | In models with implicit dependent variables, forecast the entire expression rather than the normalized variable. |

u | Substitute expressions for all auto-updating series in the equation. |

g | Graph the forecasts together with the ±2 standard error bands. |

ga | Graph the forecasts along with the actuals (if available). |

e | Produce the forecast evaluation table. |

i | Compute the forecasts of the index. Only for binary, censored and count models. |

s | Ignore ARMA terms and use only the structural part of the equation to compute the forecasts. |

n | Ignore coef uncertainty in standard error calculations that use them. |

b =arg | MA backcast method: “fa” (forecast available). Only for equations estimated with MA terms. This option is ignored if you specify the “s” (structural forecast) option. The default method uses the estimation sample. |

forcsmpl=smpl | Forecast sample (optional). If forecast sample is not provided, the workfile sample will be employed |

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). |

stochastic | Perform stochastic simulation for dynamic equations estimated using least squares. |

streps=integer (default=1000) | Number of stochastic repetitions (for threshold regression or stochastic simulation). |

stfrac=number (default=.02) | Fraction of failed repetitions before stopping (for threshold regression or stochastic simulation). |

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

p | Print view. |

Examples

The following lines:

smpl 1970q1 1990q4

equation eq1.ls con c con(-1) inc

smpl 1991q1 1995q4

eq1.fit con_s

eq1.forecast con_d

plot con_s con_d

estimate a linear regression over the period 1970Q1–1990Q4, compute static (fitted) and dynamic forecasts for the period 1991Q1–1995Q4, and plot the two forecasts in a single graph.

equation eq1.ls m1 gdp ar(1) ma(1)

eq1.forecast m1_bj bj_se

eq1.forecast(s) m1_s s_se

plot bj_se s_se

estimates an ARMA(1,1) model, computes the forecasts and standard errors with and without the ARMA terms, and plots the two forecast standard errors.

Cross-references

To perform static forecasting with equation objects see
Equation::fit. For multiple equation forecasting, see
Equation::makemodel, and
Model::solve.

For more information on equation forecasting in EViews, see
“Forecasting from an Equation”.