The varm function returns a varm object specifying the functional form and storing the parameter values of a p-order, stationary, multivariate vector autoregression model (VAR(p)) model.

This MATLAB function returns a numeric array containing paths of minimum mean squared error (MMSE) multivariate response forecasts Y over a length numperiods forecast horizon, using the fully specified VAR(p) model Mdl.

This MATLAB function converts the VEC(p – 1) model Mdl to its equivalent VAR(p) model representation VARMdl.

A varm model object is a MATLAB® variable containing properties that describe the model, such as AR polynomial degree p, response dimensionality n, and coefficient values. varm must be able to infer n and p from your specifications; n and p are not estimable.

This MATLAB function returns the fully specified, estimated VAR(p) model EstMdl.

Enter Mdl.PropertyName at the MATLAB command line, where PropertyName is the name of the property you want to examine or reassign. You can change any writable property of a model object using dot notation: ... Create a VAR(2) model object for three response variables. Use the shorthand syntax. ... Display the VAR(2) model. ... Mdl = varm with properties: Description: "3-Dimensional VAR(2) Model" SeriesNames: "Y1" "Y2" "Y3" NumSeries: 3 P: 2 Constant: [3×1 vector of NaNs] AR: {3×3 matrices of NaNs} at lags [1 2] Trend: [3×1 vector of zeros] Beta: [3×0 matrix] Covariance: [3×3 matrix of NaNs]

This MATLAB function displays a summary of the VAR(p) model Mdl.

The irf function returns the dynamic response, or the impulse response function (IRF), to a one-standard-deviation shock to each variable in a VAR(p) model. A fully specified varm model object characterizes the VAR model.

This MATLAB function converts the VAR(p) model Mdl to its equivalent VEC(p – 1) model representation VECMdl.

This MATLAB function returns a numeric array E containing the series of multivariate inferred innovations from evaluating the fully specified VAR(p) model Mdl at the numeric array of response data Y.

This MATLAB function returns the numeric array Y containing a random numobs-period path of multivariate response series from performing an unconditional simulation of the fully specified VAR(p) model Mdl.

Mdls is an 8-by-1 vector of varm objects containing estimable parameters. Mdls(4) matches the structure of the fourth model in the table. Lags 4 and 8 of the model contain coefficient matrices of NaN values, which indicates that they are estimable. All other lags have coefficient matrices of zeros, which means that they are effectively absent from the model. The Beta property is an empty matrix; MATLAB® populates Beta during estimation when you specify predictor data.

This MATLAB function returns the numeric array Y containing the multivariate response series, which results from filtering the underlying input numeric array Z containing the multivariate disturbance series.

July 17, 2022 - Hi, I am Jung struggeling with a simple problem. I am trying to apply varm to have irf. I conver my dataset to table using readtable. I just try to follow the steps using 'Data_JDanish' sugges...

TrueMdl is a fully specified varm model object, which means all parameters of its corresponding VAR(2) model are known.

Create and Adjust VAR Model Using Shorthand Syntax This example shows how to create a three-dimensional VAR(4) model with unknown parameters using varm and the shorthand syntax.

Mdl = varm with properties: Description: "3-Dimensional VAR(4) Model with Linear Time Trend" SeriesNames: "Y1" "Y2" "Y3" NumSeries: 3 P: 4 Constant: [3×1 vector of NaNs] AR: {3×3 matrices} at lags [1 4] Trend: [3×1 vector of NaNs] Beta: [3×0 matrix] Covariance: [3×3 matrix of NaNs] ... ...

numseries = 3; dnan = diag(nan(numseries,1)); VAR2diag = varm(AR={dnan dnan},SeriesNames=seriesnames); VAR2full = varm(numseries,2); VAR2full.SeriesNames = seriesnames; VAR4diag = varm(AR={dnan dnan dnan dnan},SeriesNames=seriesnames); VAR4full = varm(numseries,4); VAR4full.SeriesNames = seriesnames; The matrix dnan is a diagonal matrix with NaN values along its main diagonal. In general, missing values specify the presence of the parameter in the model, and indicate that the parameter needs to be fit to data. MATLAB® holds the off diagonal elements, 0, fixed during estimation.