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# varm

Convert vector error-correction (VEC) model to vector autoregression (VAR) model

## Syntax

``VARMdl = varm(Mdl)``

## Description

example

``VARMdl = varm(Mdl)` converts the VEC(p – 1) model `Mdl` to its equivalent VAR(p) model representation `VARMdl`.`

## Examples

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Consider a VEC model for the following seven macroeconomic series.

• Gross domestic product (GDP)

• GDP implicit price deflator

• Paid compensation of employees

• Nonfarm business sector hours of all persons

• Effective federal funds rate

• Personal consumption expenditures

• Gross private domestic investment

Suppose that a cointegrating rank of 4 and one short-run term are appropriate, that is, consider a VEC(1) model.

Load the `Data_USEconVECModel` data set.

`load Data_USEconVECModel`

For more information on the data set and variables, enter `Description` at the command line.

Determine whether the data needs to be preprocessed by plotting the series on separate plots.

```figure; subplot(2,2,1) plot(FRED.Time,FRED.GDP); title('Gross Domestic Product'); ylabel('Index'); xlabel('Date'); subplot(2,2,2) plot(FRED.Time,FRED.GDPDEF); title('GDP Deflator'); ylabel('Index'); xlabel('Date'); subplot(2,2,3) plot(FRED.Time,FRED.COE); title('Paid Compensation of Employees'); ylabel('Billions of \$'); xlabel('Date'); subplot(2,2,4) plot(FRED.Time,FRED.HOANBS); title('Nonfarm Business Sector Hours'); ylabel('Index'); xlabel('Date');``` ```figure; subplot(2,2,1) plot(FRED.Time,FRED.FEDFUNDS); title('Federal Funds Rate'); ylabel('Percent'); xlabel('Date'); subplot(2,2,2) plot(FRED.Time,FRED.PCEC); title('Consumption Expenditures'); ylabel('Billions of \$'); xlabel('Date'); subplot(2,2,3) plot(FRED.Time,FRED.GPDI); title('Gross Private Domestic Investment'); ylabel('Billions of \$'); xlabel('Date');``` Stabilize all series, except the federal funds rate, by applying the log transform. Scale the resulting series by 100 so that all series are on the same scale.

```FRED.GDP = 100*log(FRED.GDP); FRED.GDPDEF = 100*log(FRED.GDPDEF); FRED.COE = 100*log(FRED.COE); FRED.HOANBS = 100*log(FRED.HOANBS); FRED.PCEC = 100*log(FRED.PCEC); FRED.GPDI = 100*log(FRED.GPDI);```

Create a VEC(1) model using the shorthand syntax. Specify the variable names.

```Mdl = vecm(7,4,1); Mdl.SeriesNames = FRED.Properties.VariableNames```
```Mdl = vecm with properties: Description: "7-Dimensional Rank = 4 VEC(1) Model with Linear Time Trend" SeriesNames: "GDP" "GDPDEF" "COE" ... and 4 more NumSeries: 7 Rank: 4 P: 2 Constant: [7×1 vector of NaNs] Adjustment: [7×4 matrix of NaNs] Cointegration: [7×4 matrix of NaNs] Impact: [7×7 matrix of NaNs] CointegrationConstant: [4×1 vector of NaNs] CointegrationTrend: [4×1 vector of NaNs] ShortRun: {7×7 matrix of NaNs} at lag  Trend: [7×1 vector of NaNs] Beta: [7×0 matrix] Covariance: [7×7 matrix of NaNs] ```

`Mdl` is a `vecm` model object. All properties containing `NaN` values correspond to parameters to be estimated given data.

Estimate the model using the entire data set and the default options.

`EstMdl = estimate(Mdl,FRED.Variables)`
```EstMdl = vecm with properties: Description: "7-Dimensional Rank = 4 VEC(1) Model" SeriesNames: "GDP" "GDPDEF" "COE" ... and 4 more NumSeries: 7 Rank: 4 P: 2 Constant: [14.1329 8.77841 -7.20359 ... and 4 more]' Adjustment: [7×4 matrix] Cointegration: [7×4 matrix] Impact: [7×7 matrix] CointegrationConstant: [-28.6082 109.555 -77.0912 ... and 1 more]' CointegrationTrend: [4×1 vector of zeros] ShortRun: {7×7 matrix} at lag  Trend: [7×1 vector of zeros] Beta: [7×0 matrix] Covariance: [7×7 matrix] ```

`EstMdl` is an estimated `vecm` model object. It is fully specified because all parameters have known values. By default, `estimate` imposes the constraints of the H1 Johansen VEC model form by removing the cointegrating trend and linear trend terms from the model. Parameter exclusion from estimation is equivalent to imposing equality constraints to zero.

Convert the estimated VEC(1) model to its equivalent VAR(2) model representation.

`VARMdl = varm(EstMdl)`
```VARMdl = varm with properties: Description: "AR-Nonstationary 7-Dimensional VAR(2) Model" SeriesNames: "GDP" "GDPDEF" "COE" ... and 4 more NumSeries: 7 P: 2 Constant: [14.1329 8.77841 -7.20359 ... and 4 more]' AR: {7×7 matrices} at lags [1 2] Trend: [7×1 vector of zeros] Beta: [7×0 matrix] Covariance: [7×7 matrix] ```

`VARMdl` is a `varm` model object.

## Input Arguments

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VEC model, specified as a `vecm` model object created by `vecm` or `estimate`. `Mdl` must be fully specified.

## Output Arguments

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VAR model equivalent, returned as a `varm` model object.

## Algorithms

Consider the m-D VEC(p – 1) model using lag operator notation.

`$\left(1-L\right){y}_{t}=c+dt+\Pi {y}_{t-1}+\sum _{j=1}^{p-1}{\Phi }_{j}\left(1-L\right){y}_{t-j}+\beta {x}_{t}+{\epsilon }_{t}.$`

• yt is an m-by-1 vector of values corresponding to m response variables at time t, where t = 1,...,T.

• Lyt = yt – 1.

• c is the overall constant.

• d is the overall time trend coefficient.

• Π is an m-by-m impact matrix with a rank of r.

• xt is a k-by-1 vector of values corresponding to k exogenous predictor variables.

• β is an m-by-k matrix of regression coefficients.

• εt is an m-by-1 vector of random Gaussian innovations, each with a mean of 0 and collectively an m-by-m covariance matrix Σ. For ts, εt and εs are independent.

• Φj is an m-by-m matrix of short-run coefficients.

The equivalent VAR(p) model in difference equation notation is

`${y}_{t}=c+dt+\sum _{j=1}^{p}{\Gamma }_{j}{y}_{t-j}+\beta {x}_{t}+{\epsilon }_{t}.$`

Γj is an m-by-m matrix of autoregressive coefficients.

## See Also

### Topics

Introduced in R2017b

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