IRIS Toolbox Reference Manual

## jforecast

Forecast with judgmental adjustments (conditional forecasts)

#### Syntax

    F = jforecast(M,D,Range,...)

#### Input arguments

- `M` [ model ] - Solved model object.

- `D` [ struct ] - Input data from which the initial condition is taken.

- `Range` [ numeric ] - Forecast range.

#### Output arguments

- `F` [ struct ] - Output struct with the judgmentally adjusted forecast.

#### Options

- `'anticipate='` [ `true` | `false` ] - If true, real future shocks are anticipated, imaginary are unanticipated; vice versa if false.

- `'currentOnly='` [ `true` | `false` ] - If `true`, MSE matrices will be computed only for the current-dated variables, not for their lags or leads.

- `'deviation='` [ `true` | `false` ] - Treat input and output data as deviations from balanced-growth path.

- `'dtrends='` [ `'auto'` | `true` | `false` ] - Measurement data contain deterministic trends.

- `'initCond='` [ `'data'` | `'fixed'` ] - Use the MSE for the initial conditions if found in the input data or treat the initical conditions as fixed.

- `'meanOnly='` [ `true` | `false` ] - Return only mean data, i.e. point estimates.

- `'plan='` [ plan ] - Simulation plan specifying the exogenised variables and endogenised shocks.

- `'vary='` [ struct | empty ] - Database with time-varying std deviations or cross-correlations of shocks.

#### Description

When adjusting the mean and/or std devs of shocks, you can use real and imaginary numbers ot distinguish between anticipated and unanticipated shocks:

- any shock entered as an imaginary number is treated as an anticipated change in the mean of the shock distribution;

- any std dev of a shock entered as an imaginary number indicates that the shock will be treated as anticipated when conditioning the forecast on the reduced-form tunes.

- the same shock or its std dev can have both the real and the imaginary part.

#### Description

#### Example

IRIS Toolbox. Copyright © 2007—2013 Jaromir Benes.