ca.jo {urca} | R Documentation

## Johansen Procedure for VAR

### Description

Conducts the Johansen procedure on a given data set. The `"trace"` or `"eigen"` statistics are reported and the matrix of eigenvectors as well as the loading matrix.

### Usage

    ca.jo(x, type = c("eigen", "trace"), ecdet = c("none", "const", "trend"), K = 2,
    spec=c("longrun", "transitory"), season = NULL, dumvar = NULL)

### Arguments

`x` | Data matrix to be investigated for cointegration.
`type` | The test to be conducted, either ‘`eigen`’ or ‘`trace`’.
`ecdet` | Character, ‘`none`’ for no intercept in cointegration, ‘`const`’ for constant term in cointegration and ‘`trend`’ for trend variable in cointegration.
`K` | The lag order of the series (levels) in the VAR.
`spec` | Determines the specification of the VECM, see details below.
`season` | If seasonal dummies should be included, the data frequency must be set accordingly, i.e ‘4’ for quarterly data.
`dumvar` | If dummy variables should be included, a matrix with row dimension equal to `x` can be provided.

### Details

Given a general VAR of the form:

X_t = Pi_1 X_{t-1} + ... + Pi_k X_{t-k} + μ + Phi D_t + varepsilon_t , quad (t = 1, ..., T),

the following two specifications of a VECM exist:

Delta X_t = Γ_1 Delta X_{t-1} + ... + Γ_{k-1} Delta X_{t-k+1} + Pi X_{t-k} + μ + Phi D_t + varepsilon_t

where

Γ_i = - (I - Pi_1 - ... - Pi_i), quad (i = 1, ... , k-1),

and

Pi = -(I - Pi_1 - ... - Pi_k)

The Γ_i matrices contain the cumulative long-run impacts, hence if `spec="longrun"` is choosen, the above VECM is estimated.

The other VECM specification is of the form:

Delta X_t = Γ_1 Delta X_{t-1} + ... + Γ_{k-1} Delta X_{t-k+1} + Pi X_{t-1} + μ + Phi D_t + varepsilon_t

where

Γ_i = - (Pi_{i+1} + ... + Pi_k), quad(i = 1, ... , k-1),

and

Pi = -(I - Pi_1 - ... - Pi_k).

The Pi matrix is the same as in the first specification. However, the Γ_i matrices now differ, in the sense that they measure transitory effects, hence by setting `spec="transitory"` the second VECM form is estimated. Please note that inferences drawn on Pi will be the same, regardless which specification is choosen and that the explanatory power is the same, too.

If `"season"` is not NULL, centered seasonal dummy variables are included.

If `"dumvar"` is not NULL, a matrix of dummy variables is included in the VECM. Please note, that the number of rows of the matrix containing the dummy variables must be equal to the row number of `x`.

Critical values are only reported for systems with less than 11 variables and are taken from Osterwald-Lenum.

### Value

An object of class `ca.jo`.

### Author(s)

Bernhard Pfaff

### References

Johansen, S. (1988), Statistical Analysis of Cointegration Vectors, Journal of Economic Dynamics and Control, 12, 231–254.

Johansen, S. and Juselius, K. (1990), Maximum Likelihood Estimation and Inference on Cointegration – with Applications to the Demand for Money, Oxford Bulletin of Economics and Statistics, 52, 2, 169–210.

Johansen, S. (1991), Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models, Econometrica, Vol. 59, No. 6, 1551–1580.

Osterwald-Lenum, M. (1992), A Note with Quantiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistics, Oxford Bulletin of Economics and Statistics, 55, 3, 461–472.

### See Also

`plotres`, `alrtest`, `ablrtest`, `blrtest`, `cajolst`, `cajools`, `lttest`, `ca.jo-class` and `urca-class`.

### Examples

    data(denmark)
    sjd <- denmark[, c("LRM", "LRY", "IBO", "IDE")]
    sjd.vecm <- ca.jo(sjd, ecdet = "const", type="eigen", K=2, spec="longrun",
    season=4)
    summary(sjd.vecm)
    #
    data(finland)
    sjf <- finland
    sjf.vecm <- ca.jo(sjf, ecdet = "none", type="eigen", K=2,
    spec="longrun", season=4)
    summary(sjf.vecm)

[Package urca version 1.2-3 Index]