IRIS Toolbox Reference Manual

## estimate

Estimate a reduced-form VAR or BVAR

#### Syntax

    [V,VData,Fitted] = estimate(V,Inp,Range,...)

#### Input arguments

- `V` [ VAR ] - Empty VAR object.

- `Inp` [ struct ] - Input database.

- `Range` [ numeric ] - Estimation range, including `P` pre-sample periods, where `P` is the order of the VAR.

#### Output arguments

- `V` [ VAR ] - Estimated reduced-form VAR object.

- `VData` [ struct ] - Output database with the endogenous variables and the estimated residuals.

- `Fitted` [ numeric ] - Periods in which fitted values have been calculated.

#### Options

- `'A='` [ numeric | empty ] - Restrictions on the individual values in the transition matrix, `A`.

- `'BVAR='` [ numeric ] - Prior dummy observations for estimating a BVAR; construct the dummy observations using the one of the `BVAR` functions.

- `'C='` [ numeric | empty ] - Restrictions on the individual values in the constant vector, `C`.

- `'diff='` [ `true` | `false` ] - Difference the series before estimating the VAR; integrate the series back afterwards.

- `'G='` [ numeric | empty ] - Restrictions on the individual values in the matrix at the co-integrating vector, `G`.

- `'cointeg='` [ numeric | empty ] - Co-integrating vectors (in rows) that will be imposed on the estimated VAR.

- `'comment='` [ char | `Inf` ] - Assign comment to the estimated VAR object; `Inf` means the existing comment will be preserved.

- `'constraints='` [ char ] - General linear constraints on the VAR parameters.

- `'constant='` [ `true` | `false` ] - Include a constant vector in the VAR.

- `'covParameters='` [ `true` | `false` ] - Calculate the covariance matrix of estimated parameters.

- `'eqtnByEqtn='` [ `true` | `false` ] - Estimate the VAR equation by equation.

- `'maxIter='` [ numeric | `1` ] - Maximum number of iterations when generalised least squares algorithm is involved.

- `'mean='` [ numeric | empty ] - Impose a particular asymptotic mean on the VAR process.

- `'order='` [ numeric | `1` ] - Order of the VAR.

- `'progress='` [ `true` | `false` ] - Display progress bar in the command window.

- `'schur='` [ `true` | `false` ] - Calculate triangular (Schur) representation of the estimated VAR straight away.

- `'stdize='` [ `true` | `false` ] - Adjust the prior dummy observations by the std dev of the observations.

- `'timeWeights=`' [ tseries | empty ] - Time series of weights applied to individual periods in the estimation range.

- `'tolerance='` [ numeric | `1e-5` ] - Convergence tolerance when generalised least squares algorithm is involved.

- `'warning='` [ `true` | `false` ] - Display warnings produced by this function.

#### Options for panel VAR

- `'fixedEffect='` [ `true` | `false` ] - Include constant dummies for fixed effect in panel estimation; applies only if `'constant=' true`.

- `'groupWeights='` [ numeric | empty ] - A 1-by-NGrp vector of weights applied to groups in panel estimation, where NGrp is the number of groups; the weights will be rescaled so as to sum up to `1`.

#### Description

##### Estimating a panel VAR

Panel VAR objects are created by calling the function `VAR` with two input arguments: the list of variables, and the list of group names. To estimate a panel VAR, the input data, `Inp`, must be organised a super-database with sub-databases for each group, and time series for each variables within each group:

    d.Group1_Name.Var1_Name
    d.Group1_Name.Var2_Name
    ...
    d.Group2_Name.Var1_Name
    d.Group2_Name.Var2_Name
    ...

#### Example

IRIS Toolbox. Copyright © 2007—2013 Jaromir Benes.