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 Modeling the United States Economy.
|Econometric Modeler||Analyze and model econometric time series|
Fit Model to Data
Convert Between Models
Generate Simulations or Impulse Responses
Generate Minimum Mean Square Error Forecasts
- Econometric Modeler App Overview
The Econometric Modeler app is an interactive tool for visualizing and analyzing univariate or multivariate time series data.
- Conduct Cointegration Test Using Econometric Modeler App
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 App
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.
- Modeling the United States Economy
This example illustrates the use of a vector error-correction (VEC) model as a linear alternative to the Smets-Wouters Dynamic Stochastic General Equilibrium (DSGE) macroeconomic model, and applies many of the techniques of Smets-Wouters to the description of the United States economy.
- 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.