Overview of VAR objects

VAR objects can be constructed as plain VARs or simple panel VARs (with fixed effect), and estimated without or with prior dummy observations (quasi-bayesian VARs). VAR objects are reduced-form models but they are also the point of departure for identifying structural VARs

VAR models in IRIS have the following form:

\[ y_t = \sum_{k=1}^{p} A_k\, y_{t-k} + K + J g_t + \epsilon_t \]

where

\(y_t\) is an \(n\)-by-1 vector of endogenous variables;
\(A_k\) are transition matrices at lags 1, ..., k;
\(K\) is a vector of intercepts;
\(g_t\) is a vector of exogenous variables;
\(J\) is the impact matrix of exogenous variables;
\(\epsilon_t\) is a vector of forecast (reduced-form) errors, with \(\Omega=\mathrm{E}[\epsilon_t \epsilon_t']\).

Categorical list

Constructor

Function	Description
`VAR`	Create new empty reduced-form VAR object

Properties Directly Accessible

Function	Description
`object.A`	Transition matrices with higher orders concatenated horizontally
`object.K`	Vector of intercepts (constant terms)
`object.J`	Impact matrix of exogenous variables
`object.Omega`	Covariance matrix of reduced-form forecast errors
`object.Sigma`	Covariance matrix of parameter estimates
`object.AIC`	Akaike information criterion
`object.AICc`	Akaike information criterion corrected for small sample
`object.SBC`	Schwarz bayesian criterion
`object.EigenValues`	Eigenvalues of VAR transition matrix
`object.EigenStability`	Stability indicator for each eigenvalue
`object.Range`	Estimation range entered by user
`object.InxFitted`	Logical index of dates in estimation range acutally fitted
`object.EndogenousNames`	Names of endogenous variables
`object.ResidualNames`	Names of errors
`object.ExogenousNames`	Names of exogenous variables
`object.GroupNames`	Names of groups in panel VARs
`object.ConditioningNames`	Names of conditioning instruments
`object.NumEndogenous`	Number of endogenous variables
`object.NumResiduals`	Number of errors
`object.NumExogenous`	Number of exogenous variables
`object.NumGroups`	Number of groups in panel VARs
`object.NumConditioning`	Number of conditioning instruments

Getting Information about VAR Objects

Function	Description
`addToDatabank`	Add VAR parameters to databank or create new databank
`comment`	Get or set user comments in an IRIS object
`companion`	Matrices of first-order companion VAR
`eig`	Eigenvalues of a VAR process
`fprintf`	Write VAR model as formatted model code to text file
`get`	Query VAR object properties
`testCompatible`	True if two VAR objects can occur together on the LHS and RHS in an assignment
`isexplosive`	True if any eigenvalue is outside unit circle
`isstationary`	True if all eigenvalues are within unit circle
`length`	Number of parameter variants in VAR object
`mean`	Asymptotic mean of VAR process
`nfitted`	Number of data points fitted in VAR estimation
`rngcmp`	True if two VAR objects have been estimated using the same dates
`sprintf`	Print VAR model as formatted model code
`sspace`	Quasi-triangular state-space representation of VAR
`userdata`	Get or set user data in an IRIS object

Referencing VAR Objects

Function	Description
`group`	Retrieve VAR object from panel VAR for specified group of data
`subsasgn`	Subscripted assignment for VAR objects
`subsref`	Subscripted reference for VAR objects

Simulation, Forecasting and Filtering

Function	Description
`ferf`	Forecast error response function
`filter`	Filter data using a VAR model
`forecast`	Unconditional or conditional VAR forecasts
`instrument`	Define forecast conditioning instruments in VAR models
`resample`	Resample from a VAR object
`simulate`	Simulate VAR model

Manipulating VARs

Function	Description
`assign`	Manually assign system matrices to VAR object
`alter`	Expand or reduce the number of alternative parameterisations within a VAR object
`backward`	Backward VAR process
`demean`	Remove constant and the effect of exogenous inputs from VAR object
`horzcat`	Combine two compatible VAR objects in one object with multiple parameterisations integrate - Integrate VAR process and data associated with it
`xasymptote`	Set or get asymptotic assumptions for exogenous inputs

Stochastic Properties

Function	Description
`acf`	Autocovariance and autocorrelation functions for VAR variables
`fmse`	Forecast mean square error matrices
`vma`	Matrices describing the VMA representation of a VAR process
`xsf`	Power spectrum and spectral density functions for VAR variables

Estimation, Identification, and Statistical Tests

Function	Description
`estimate`	Estimate a reduced-form VAR or BVAR
`infocrit`	Populate information criteria for a parameterised VAR
`lrtest`	Likelihood ratio test for VAR models
`portest`	Portmanteau test for autocorrelation in VAR residuals
`schur`	Compute and store triangular representation of VAR