Vector Error-Correction Models

Multivariate linear models including cointegrating relations and exogenous predictor variables

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.

Functions

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Create Model

vecm Create vector error-correction (VEC) model

Fit Model to Data

`estimate`	Fit vector error-correction (VEC) model to data
`infer`	Infer vector error-correction (VEC) model innovations
`summarize`	Display estimation results of vector error-correction (VEC) model

Convert Between Models

`arma2ar`	Convert ARMA model to AR model
`arma2ma`	Convert ARMA model to MA model
`vec2var`	Convert VEC model to VAR model
`var2vec`	Convert VAR model to VEC model
`varm`	Convert vector error-correction (VEC) model to vector autoregression (VAR) model

Generate Simulations or Impulse Responses

`simulate`	Monte Carlo simulation of vector error-correction (VEC) model
`filter`	Filter disturbances through vector error-correction (VEC) model
`irf`	Generate vector error-correction (VEC) model impulse responses
`fevd`	Generate vector error-correction (VEC) model forecast error variance decomposition (FEVD)

Generate Minimum Mean Square Error Forecasts

forecast Forecast vector error-correction (VEC) model responses

Topics

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.

Documentation