Vector Error-Correction Models
Vector-error correction (VEC) models, or cointegrated VAR models, address nonstationarity in multivariate time series resulting from co-movements of multiple response series. For an example of an analysis using VEC modeling tools, see Model the United States Economy.
|Analyze and model econometric time series
Fit Model to Data
Convert Between Models
Generate Simulations or Impulse Responses
|Monte Carlo simulation of vector error-correction (VEC) model
|Filter disturbances through vector error-correction (VEC) model
|Generate vector error-correction (VEC) model impulse responses (Since R2019a)
|Generate vector error-correction (VEC) model forecast error variance decomposition (FEVD) (Since R2019a)
Generate Minimum Mean Square Error Forecasts
- Analyze Time Series Data Using Econometric Modeler
Interactively visualize and analyze univariate or multivariate time series data.
- Conduct Cointegration Test Using Econometric Modeler
Interactively test series for cointegration by using the Engle-Granger cointegration test and the Johansen cointegration test.
- Specifying Multivariate Lag Operator Polynomials and Coefficient Constraints Interactively
Specify multivariate lag operator polynomial terms for time series model estimation using Econometric Modeler.
- Estimate Vector Error-Correction Model Using Econometric Modeler
Interactively fit several vector error-correction (VEC) models to data. Then, select an estimated model and export it to the command line to generate forecasts.
- Model the United States Economy
Use a vector error-correction model as a linear alternative to the Smets-Wouters DSGE macroeconomic model.
- Generate VEC Model Impulse Responses
Generate impulse responses from a VEC model.
- VEC Model Monte Carlo Forecasts
Generate Monte Carlo and MMSE forecasts from a VEC model.