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Vector Autoregression Models

Stationary multivariate linear models including exogenous predictor variables

A vector autoregression (VAR) model is a system of simultaneous, linear equations that describe the evolution of multiple, stationary response series. Equations in the system are functions of constants, time trends, lagged responses, and exogenous predictor variables. For an example of an analysis using VAR modeling tools, see VAR Model Case Study.


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varmCreate vector autoregression (VAR) model
estimateFit vector autoregression (VAR) model to data
inferInfer vector autoregression model (VAR) innovations
summarizeDisplay estimation results of vector autoregression (VAR) model
gctestGranger causality and block exogeneity tests for vector autoregression (VAR) models
irfGenerate vector autoregression (VAR) model impulse responses
fevdGenerate vector autoregression (VAR) model forecast error variance decomposition (FEVD)
gctestBlock-wise Granger causality and block exogeneity tests
armairfGenerate or plot ARMA model impulse responses
armafevdGenerate or plot ARMA model forecast error variance decomposition (FEVD)
arma2arConvert ARMA model to AR model
arma2maConvert ARMA model to MA model
vec2varConvert VEC model to VAR model
var2vecConvert VAR model to VEC model
vecmConvert vector autoregression (VAR) model to vector error-correction (VEC) model
simulateMonte Carlo simulation of vector autoregression (VAR) model
filterFilter disturbances through vector autoregression (VAR) model
forecastForecast vector autoregression (VAR) model responses


Create Model

Vector Autoregression (VAR) Models

Learn the characteristics of vector autoregression models and how to create them.

Multivariate Time Series Model Creation

Learn how to create VAR models using varm.

Fit Model to Data

VAR Model Estimation

Fit VAR models to data.

Multivariate Time Series Data Structures

Prepare your data for a multivariate time series analysis.

Fit VAR Model to Simulated Data

Simulate data from a known VAR model, then fit a VAR model to the simulated data.

Fit VAR Model of CPI and Unemployment Rate

Estimate a VAR model composed of the consumer price index and unemployment rate.

Implement Seemingly Unrelated Regression

Include exogenous predictors in a VAR model to estimate a regression component along with all other parameters.

Estimate Capital Asset Pricing Model Using SUR

Implement the capital asset pricing model (CAPM) using the Econometrics Toolbox™ VAR model framework.

VAR Model Case Study

Analyze a VAR model.

Convert Between Models

Convert VARMA Model to VAR Model

Create a VARMA model, and then convert it to a pure VAR model.

Generate Simulations or Impulse Responses

VAR Model Forecasting, Simulation, and Analysis

Use models to extrapolate the behavior of time series.

Generate VAR Model Impulse Responses

Generate impulse responses of an interest rate shock on real GDP.

Compare Generalized and Orthogonalized Impulse Response Functions

Demonstrate differences between orthogonal and generalized impulse response functions.

Simulate VAR Model Conditional Responses

Forecast CPI growth rates given known values of the unemployment rate using Monte Carlo simulation.

Simulate Responses Using filter

Reproduce the results of simulate using filter.

Simulate Responses of Estimated VARX Model

Estimate a multivariate time series model that contains lagged endogenous and exogenous variables and simulate responses.

Forecast VAR Model Using Monte Carlo Simulation

Generate forecasts from a VAR model using Monte Carlo simulation.

Generate Minimum Mean Square Error Forecasts

Forecast VAR Model

Generate forecasts with error estimates.

Forecast VAR Model Using Monte Carlo Simulation

Generate forecasts from a VAR model using Monte Carlo simulation.

Forecast VAR Model Conditional Responses

Forecast responses given contemporaneous information about other response values in the forecast horizon.