jcontest

Johansen constraint test

Syntax

``h = jcontest(Y,r,test,Cons)``
``````[h,pValue,stat,cValue] = jcontest(Y,r,test,Cons)``````
``StatTbl = jcontest(Tbl,r,test,Cons)``
``[___] = jcontest(___,Name=Value)``
``````[___,mles] = jcontest(___)``````

Description

example

````h = jcontest(Y,r,test,Cons)` returns rejection decisions from conducting the Johansen constraint test, which assesses linear constraints on either the error-correction (adjustment) speeds A or the cointegration space spanned by the cointegrating matrix B in the reduced-rank VEC(q) model of a multivariate time series. The following list describes the quantities in the signature, `Y` is a matrix of observations of yt.`r` is the common rank of matrices A and B.`test` specifies the constraint types, including linear or equality constraints on A or B.`Cons` specifies the test constraint values. For a particular test, the constraint type and values form the null hypotheses tested against the alternative hypothesis H(r) of cointegration rank less than or equal to r (an unconstrained VEC model). The tests also produce maximum likelihood estimates of the parameters in the VEC(q) model, subject to the constraints.Each element of `test` and `Cons` results in a separate test.```

example

``````[h,pValue,stat,cValue] = jcontest(Y,r,test,Cons)``` also returns the p-values `pValue`, test statistics `stat`, and critical values `cValue` of the test.```

example

````StatTbl = jcontest(Tbl,r,test,Cons)` returns a table containing variables for the test results, statistics, and settings from conducting the Johansen constraint test on all variables of an input table or timetable.To select a subset of variables in `Tbl` to test, use the `DataVariables` name-value argument. ```

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````[___] = jcontest(___,Name=Value)` specifies options using one or more name-value arguments in addition to any of the input argument combinations in previous syntaxes. `jcontest` returns the output argument combination for the corresponding input arguments.In addition to `bp`, some options control the number of tests to conduct.For example, `jcontest(Tbl,r,test,Cons,Model="H2",DataVariables=1:5)` tests the first 5 variables in the input table `Tbl` using the Johansen model that excludes all deterministic terms.```

example

``````[___,mles] = jcontest(___)``` also returns a structure of maximum likelihood estimates associated with the constrained VEC(q) models of the multivariate time series yt.```

Examples

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Test for weak exogeneity of a time series, with respect to other series in the system, by using default options of `jcontest`. Input the time series data as a numeric matrix.

Load data of Canadian inflation and interest rates `Data_Canada.mat`, which contains the series in the matrix `Data`.

```load Data_Canada series'```
```ans = 5x1 cell {'(INF_C) Inflation rate (CPI-based)' } {'(INF_G) Inflation rate (GDP deflator-based)'} {'(INT_S) Interest rate (short-term)' } {'(INT_M) Interest rate (medium-term)' } {'(INT_L) Interest rate (long-term)' } ```

Use the Johansen constraint test to assess whether the CPI-based inflation rate ${\mathit{y}}_{1,\mathit{t}}$ is weakly exogenous with respect to the three interest rate series by testing the following constraint in a 4-D VEC model of the series:

`$\left(1-L\right){y}_{1,t}=c+{\epsilon }_{t}.$`

Specify a rank of 1 for the test, a linear constraint on the 4-by-1 adjustment speed vector$\mathit{A}$ so that ${\mathit{a}}_{1}=0$, and default options. Return the rejection decision.

```Cons = [1; 0; 0; 0]; Y = Data(:,[1 3:5]); h = jcontest(Y,1,"ACon",Cons)```
```h = logical 0 ```

Given default options and assumptions, `h = 0` suggests that the test fails to reject the null hypothesis of the constrained model, which is that the inflation rate is weakly exogenous with respect to the interest rate series.

Load data of Canadian inflation and interest rates `Data_Canada.mat`, which contains the series in the matrix `Data`.

`load Data_Canada`

Conduct the default Johansen constraint test to assess whether the CPI-based inflation rate is weakly exogenous with respect to the interest rate series. Return the test decisions and $\mathit{p}$-values.

```Cons = [1; 0; 0; 0]; Y = Data(:,[1 3:5]); [h,pValue,stat,cValue] = jcontest(Y,1,"ACon",Cons)```
```h = logical 0 ```
```pValue = 0.3206 ```
```stat = 0.9865 ```
```cValue = 3.8415 ```

Test for weak exogeneity of a time series, which are variables in a table, with respect to the other time series in the table. Return a table of results.

Load data of Canadian inflation and interest rates `Data_Canada.mat`. Convert the table `DataTable` to a timetable.

```load Data_Canada dates = datetime(dates,ConvertFrom="datenum"); TT = table2timetable(DataTable,RowTimes=dates); TT.Observations = [];```

Use the Johansen constraint test to assess whether the CPI-based and GDP-deflator-based inflation rates (${\mathit{y}}_{1,\mathit{t}}$ and ${\mathit{y}}_{2,\mathit{t}}$, respectively) are weakly exogenous with respect to the three interest rate series by testing the following constraint in a 5-D VEC model of the series:

`$\begin{array}{l}\left(1-L\right){y}_{1,t}={c}_{1,1}+{\epsilon }_{1,t}\\ \left(1-L\right){y}_{2,t}={c}_{2,1}+{\epsilon }_{2,t}.\end{array}$`

Specify a rank of 2 for the test, a linear constraint on the 4-by-2 adjustment speed vector$\mathit{A}$ so that ${\mathit{a}}_{1}=0$ and ${\mathit{a}}_{2}=0$, and default options.

```Cons = [1 0; 0 1; 0 0; 0 0; 0 0]; StatTbl = jcontest(TT,2,"ACon",Cons)```
```StatTbl=1×8 table h pValue stat cValue Lags Alpha Model Test _____ __________ ______ ______ ____ _____ ______ ________ Test 1 true 1.3026e-05 27.907 9.4877 0 0.05 {'H1'} {'acon'} ```

`StatTbl.h = 1` means that the test rejects the null hypothesis of the constrained model that the inflation rates are jointly weakly exogenous. `StatTbl.pValue = 1.3026e-5` suggests that the evidence to reject is strong.

By default, `jcontest` conducts the Johansen constraint test on all variables in the input table. To select a subset of variables from an input table, set the `DataVariables` option.

Use the Johansen framework to test multivariate time series with the following characteristics:

1. The log Australian CPI, log US CPI, and the exchange rate series of the countries are stationary.

2. The three series exhibit cointegration.

3. The Australian and the US dollars have the same purchasing power.

Load and Inspect Data

Load the data on Australian and U.S. prices `Data_JAustralian.mat`, which contains the table `DataTable`. Convert the table to a timetable.

```load Data_JAustralian dates = datetime(dates,ConvertFrom="datenum"); TT = table2timetable(DataTable,RowTimes=dates); TT.Dates = [];```

Plot the log Australian and log US CPI series (`PAU` and `PUS`, respectively), and the log AUD/USD exchange rate series `EXCH`.

```varnames = ["PAU" "PUS" "EXCH"]; plot(TT.Time,TT{:,varnames}) legend(varnames,Location="best") grid on```

Pretest for Stationarity

Use `jcontest` to test the null hypothesis that the individual series are stationary by specifying, for each variable $\mathit{j}$, the following constrained model

`$\left(1-L\right){y}_{t}=\left[\begin{array}{c}{a}_{1,j}\\ {a}_{2,j}\\ {a}_{3,j}\end{array}\right]\left({y}_{t-1}+{c}_{0}\right)+{c}_{1}+{\epsilon }_{t}.$`

Specify the variables to use in the test.

`Cons = num2cell(eye(3),1)`
```Cons=1×3 cell array {3x1 double} {3x1 double} {3x1 double} ```
`StatTbl0 = jcontest(TT,1,"BVec",Cons,DataVariables=varnames)`
```StatTbl0=3×8 table h pValue stat cValue Lags Alpha Model Test _____ __________ ______ ______ ____ _____ ______ ________ Test 1 true 1.307e-05 22.49 5.9915 0 0.05 {'H1'} {'bvec'} Test 2 true 1.0274e-05 22.972 5.9915 0 0.05 {'H1'} {'bvec'} Test 3 false 0.06571 5.445 5.9915 0 0.05 {'H1'} {'bvec'} ```

`jcontest` returns a table of test results. Each row corresponds to a separate test and columns correspond to results or specified options for each test.

`StatTbl.h(``j``) = 1` rejects the null hypothesis of stationarity of variable `j`, and `StatTbl.h(``j``) = 0` fails to reject stationarity.

Test for Cointegration

Test for cointegration by using `jcitest`.

`StatTbl1 = jcitest(TT,DataVariables=varnames)`
```StatTbl1=1×7 table r0 r1 r2 Model Lags Test Alpha _____ _____ _____ ______ ____ _________ _____ t1 true false false {'H1'} 0 {'trace'} 0.05 ```

`StatTbl1.r1 = 0` and `StatTbl1.r2 = 0` suggest that the series exhibit at least rank 1 cointegration.

Test for Purchasing Power Parity

Test for purchasing power parity `PAU = PUS + EXCH`.

`StatTbl2 = jcontest(TT,1,"BCon",[1 -1 -1]',DataVariables=varnames)`
```StatTbl2=1×8 table h pValue stat cValue Lags Alpha Model Test _____ ________ ______ ______ ____ _____ ______ ________ Test 1 false 0.053995 3.7128 3.8415 0 0.05 {'H1'} {'bcon'} ```

`StatTbl2.h = 0` means that the test fails to reject the null hypothesis of the constrained model, that is, purchasing power parity between the models should not be rejected.

Compare the four types of supported constraints on the adjustment-speed and cointegrating matrices.

Load the data on Australian and US prices `Data_JAustralian.mat`, which contains the table `DataTable`. Convert the table to a timetable. Consider a 3-D VEC model consisting of the log Australian and US CPI, and the log AUD/USD exchange rate series.

```load Data_JAustralian dates = datetime(dates,ConvertFrom="datenum"); TT = table2timetable(DataTable,RowTimes=dates); TT.Dates = []; varnames = ["PAU" "PUS" "EXCH"];```

Conduct the four Johansen constraint tests; specify the arbitrary constraint value $\left[\begin{array}{ccc}1& -1& -1\end{array}\right]\prime$. Return the test results and maximum likelihood estimates of the constrained model.

```[StatTbl,mle] = jcontest(TT,1,["ACon" "AVec" "BCon" "BVec"], ... [1 -1 -1]',DataVariables=varnames); StatTbl```
```StatTbl=4×8 table h pValue stat cValue Lags Alpha Model Test _____ __________ ______ ______ ____ _____ ______ ________ Test 1 false 0.11047 2.5475 3.8415 0 0.05 {'H1'} {'acon'} Test 2 true 3.0486e-08 34.612 5.9915 0 0.05 {'H1'} {'avec'} Test 3 false 0.053995 3.7128 3.8415 0 0.05 {'H1'} {'bcon'} Test 4 false 0.074473 5.1946 5.9915 0 0.05 {'H1'} {'bvec'} ```

`mle` is a 4-by-1 structure array with fields containing the maximum likelihood estimates of the constrained models for each test.

For each test, display the estimates of $\mathit{A}$ and $\mathit{B}$, and compute the MLE of the impact matrix $\stackrel{ˆ}{\text{\hspace{0.17em}}\mathit{C}}=\stackrel{ˆ}{\mathit{A}}\stackrel{ˆ}{\text{\hspace{0.17em}}\mathit{B}}\prime$. The `impactmat` function is a local supporting function that computes the MLE of the impact matrix and displays the estimated matrices.

`[AACon,BACon,CACon] = impactmat(mle(1))`
```AACon = 3×1 0.0043 0.0055 -0.0012 ```
```BACon = 3×1 2.8496 -2.3341 -6.2670 ```
```CACon = 3×3 0.0121 -0.0099 -0.0267 0.0156 -0.0128 -0.0343 -0.0035 0.0028 0.0076 ```
`[1 -1 -1]*AACon`
```ans = 0 ```
`[AAVec,BAVec,CAVec] = impactmat(mle(2))`
```AAVec = 3×1 1 -1 -1 ```
```BAVec = 3×1 -0.0204 0.0158 0.0246 ```
```CAVec = 3×3 -0.0204 0.0158 0.0246 0.0204 -0.0158 -0.0246 0.0204 -0.0158 -0.0246 ```
`[ABCon,BBCon,CBCon] = impactmat(mle(3))`
```ABCon = 3×1 -0.0043 -0.0052 -0.0089 ```
```BBCon = 3×1 1.8001 -3.9210 5.7211 ```
```CBCon = 3×3 -0.0078 0.0170 -0.0248 -0.0094 0.0206 -0.0300 -0.0159 0.0347 -0.0507 ```
`[1 -1 -1]*BBCon`
```ans = 4.4409e-16 ```
`[ABVec,BBVec,CBVec] = impactmat(mle(4))`
```ABVec = 3×1 0.0252 0.0422 0.0556 ```
```BBVec = 3×1 1 -1 -1 ```
```CBVec = 3×3 0.0252 -0.0252 -0.0252 0.0422 -0.0422 -0.0422 0.0556 -0.0556 -0.0556 ```

Observe that the `AVec` and `BVec` constraints apply the constraint value directly to the coefficients, whereas the estimates of the `ACon` and `BCon` constraints fulfill the corresponding linear constraint.

Supporting Function

```function [A,B,C] = impactmat(mlest) A = mlest.paramVals.A; B = mlest.paramVals.B; C = A*B'; end```

Input Arguments

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Data representing observations of a multivariate time series yt, specified as a `numObs`-by-`numDims` numeric matrix. Each column of `Y` corresponds to a variable, and each row corresponds to an observation.

Data Types: `double`

Data representing observations of a multivariate time series yt, specified as a table or timetable with `numObs` rows. Each row of `Tbl` is an observation.

To select a subset of variables in `Tbl` to test, use the `DataVariables` name-value argument.

Common rank of A and B, specified by a positive integer in the interval [1,`numDims` − 1].

Tip

Infer `r` by conducting a Johansen test using `jcitest`.

Data Types: `double`

Null hypothesis constraint type, specified as a constraint name in the table, or a string vector or cell vector of character vectors of such values.

Constraint NameDescription
`"ACon"`Test linear constraints on A.
`"AVec"`Test specific vectors in A.
`"BVec"`Test linear constraints on B.
`"BVec"`Test specific vectors in B.

`jcontest` conducts a separate test for each value in `test`.

Data Types: `string` | `char` | `cell`

Null hypothesis constraint values, specified as a value for the corresponding constraint type `test`, or a cell vector of such values, in this table.

For constraints on B, the number of rows in each matrix `numDims1` is one of the following, where `numDims` is the number of dimensions in the input data:

• `numDims + 1` when the `Model` name-value argument is `"H*"` or `"H1*"` and constraints include the restricted deterministic term in the model

• `numDims` otherwise

Constraint Type `test`Constraint Value `Cons`Description
`"ACon"`R, a `numDims`-by-`numCons` numeric matrixSpecifies `numCons` constraints on A given by R'A = 0, where `numCons``numDims``r`.
`"AVec"``numDims1`-by-`numCons` numeric matrixSpecifies `numCons` equality constraints imposed on error-correction speed vectors in A, where `numCons``r`.
`"BCon"`R, `numDims`-by-`numCons` numeric matrix Specifies `numCons` constraints on B given by R'B = 0, where `numCons``numDims``r`.
`"BVec"``numDims1`-by-`numCons` numeric matrixSpecifies `numCons` equality constraints imposed on `numCons` of the cointegrating vectors in B, where `numCons``r`.

Tip

When you construct constraint values, use the following interpretations of the rows and columns of A and B.

• Row i of A contains the adjustment speeds of variable yi,t to disequilibrium in each of the `r` cointegrating relations.

• Column j of A contains the adjustment speeds of each of the `numDims` variables to disequillibrium in cointegrating relation j.

• Row i of B contains the coefficients of variable yi,t in each of the `r` cointegrating relations.

• Column j of B contains the coefficients of each of the `numDims` variables in cointegrating relation j.

`jcontest` conducts a separate test for each cell in `Cons`.

Data Types: `string` | `char` | `cell`

Note

`jcontest` removes the following observations from the specified data:

• All rows containing at least one missing observation, represented by a `NaN` value

• From the beginning of the data, initial values required to initialize lagged variables

Name-Value Arguments

Specify optional pairs of arguments as `Name1=Value1,...,NameN=ValueN`, where `Name` is the argument name and `Value` is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose `Name` in quotes.

Example: `jcontest(Tbl,r,test,Cons,Model="H2",DataVariables=1:5)` tests the first 5 variables in the input table `Tbl` using the Johansen model that excludes all deterministic terms.

Johansen form of the VEC(q) model deterministic terms [3], specified as a Johansen form name in the table, or a string vector or cell vector of character vectors of such values (for model parameter definitions, see Vector Error-Correction (VEC) Model).

ValueError-Correction TermDescription
`"H2"`

AB´yt − 1

No intercepts or trends are present in the cointegrating relations, and no deterministic trends are present in the levels of the data.

Specify this model only when all response series have a mean of zero.

`"H1*"`

A(B´yt−1+c0)

Intercepts are present in the cointegrating relations, and no deterministic trends are present in the levels of the data.

`"H1"`

A(B´yt−1+c0)+c1

Intercepts are present in the cointegrating relations, and deterministic linear trends are present in the levels of the data.

`"H*"`A(B´yt−1+c0+d0t)+c1

Intercepts and linear trends are present in the cointegrating relations, and deterministic linear trends are present in the levels of the data.

`"H"`A(B´yt−1+c0+d0t)+c1+d1t

Intercepts and linear trends are present in the cointegrating relations, and deterministic quadratic trends are present in the levels of the data.

If quadratic trends are not present in the data, this model can produce good in-sample fits but poor out-of-sample forecasts.

`jcontest` conducts a separate test for each value in `Model`.

Example: `Model="H1*"` uses the Johansen form `H1*` for all tests.

Example: `Model=["H1*" "H1"]` uses Johansen form `H1*` for the first test, and then uses Johansen form `H1` for the second test.

Data Types: `string` | `char` | `cell`

Number of lagged differences q in the VEC(q) model, specified as a nonnegative integer or vector of nonnegative integers.

`jcontest` conducts a separate test for each value in `Lags`.

Example: `Lags=1` includes Δyt – 1 in the model for all tests.

Example: `Lags=[0 1]` includes no lags in the model for the first test, and then includes Δyt – 1 in the model for the second test.

Data Types: `double`

Nominal significance level for the hypothesis test, specified as a numeric scalar between `0.001` and `0.999` or a numeric vector of such values.

`jcontest` conducts a separate test for each value in `Alpha`.

Example: `Alpha=[0.01 0.05]` uses a level of significance of `0.01` for the first test, and then uses a level of significance of `0.05` for the second test.

Data Types: `double`

Variables in `Tbl` for which `jcontest` conducts the test, specified as a string vector or cell vector of character vectors containing variable names in `Tbl.Properties.VariableNames`, or an integer or logical vector representing the indices of names. The selected variables must be numeric.

Example: `DataVariables=["GDP" "CPI"]`

Example: `DataVariables=[true true false false]` or `DataVariables=[1 2]` selects the first and second table variables.

Data Types: `double` | `logical` | `char` | `cell` | `string`

Note

• When `jcontest` conducts multiple tests, the function applies all single settings (scalars or character vectors) to each test.

• All vector-valued specifications that control the number of tests must have equal length.

• If you specify the vector `y` and any value is a row vector, all outputs are row vectors.

• A lagged and differenced time series has a reduced sample size. Absent presample values, if the test series yt is defined for t = 1,…,T, the lagged series yt– k is defined for t = k+1,…,T. The first difference applied to the lagged series yt– k further reduces the time base to k+2,…,T. With p lagged differences, the common time base is p+2,…,T and the effective sample size is T–(p+1).

Output Arguments

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Test rejection decisions, returned as a logical scalar or vector with length equal to the number of tests. `jcontest` returns `h` when you supply the input `Y`.

• Values of `1` indicate rejection of the null hypothesis that the specified constraints `test` and `Cons` hold in favor of the alternative hypothesis that they do not hold.

• Values of `0` indicate failure to reject the null hypothesis that the constraints hold.

Test statistic p-values, returned as a numeric scalar or vector with length equal to the number of tests. `jcontest` returns `pValue` when you supply the input `Y`.

The p-values are right-tail probabilities.

Test statistics, returned as a numeric scalar or vector with length equal to the number of tests. `jcontest` returns `stat` when you supply the input `Y`.

The test statistics are likelihood ratios determined by the test.

Critical values, returned as a numeric scalar or vector with length equal to the number of tests. `jcontest` returns `cValue` when you supply the input `Y`.

The asymptotic distributions of the test statistics are chi-square, with the degree-of-freedom parameter determined by the test. The critical value of the test statistics are for a right-tail probability.

Test summary, returned as a table with variables for the outputs `h`, `pValue`, `stat`, and `cValue`, and with a row for each test. `jcontest` returns `StatTbl` when you supply the input `Tbl`.

`StatTbl` contains variables for the test settings specified by `Lags`, `Alpha`, `Model`, and `Test`.

Maximum likelihood estimates associated with the constrained VEC(q) model of yt, return as a structure array with the number of records equal to the number of tests.

Each element of `mles` has the fields in this table. You can access a field using dot notation, for example, `mles(3).paramVals` contains a structure of the parameter estimates.

FieldDescription
`paramNames`

Cell vector of parameter names, of the form:

{`A`, `B`, `B1`, … `Bq`, `c0`, `d0`, `c1`, `d1`}.

Elements depend on the values of `Lags` and `Model`.

`paramVals`Structure of parameter estimates with field names corresponding to the parameter names in `paramNames`.
`res` T-by-`numDims` matrix of residuals, where T is the effective sample size, obtained by fitting the VEC(q) model of yt to the input data.
`EstCov`Estimated covariance Q of the innovations process εt.
`rLL` Restricted loglikelihood of yt under the null hypothesis.
`uLL`Unrestricted loglikelihood of yt under the alternative hypothesis.
`dof`Degrees of freedom of the asymptotic chi-square distribution of the test statistic.

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Vector Error-Correction (VEC) Model

A vector error-correction (VEC) model is a multivariate, stochastic time series model consisting of a system of m = `numDims` equations of m distinct, differenced response variables. Equations in the system can include an error-correction term, which is a linear function of the responses in levels used to stabilize the system. The cointegrating rank r is the number of cointegrating relations that exist in the system.

Each response equation can include a degree q autoregressive polynomial composed of first differences of the response series, a constant, a time trend, and a constant and time trend in the error-correction term.

Expressed in lag operator notation, a VEC(q) model for a multivariate time series yt is

`$\begin{array}{c}\Phi \left(L\right)\left(1-L\right){y}_{t}=A\left(B\prime {y}_{t-1}+{c}_{0}+{d}_{0}t\right)+{c}_{1}+{d}_{1}t+{\epsilon }_{t}\\ =c+dt+C{y}_{t-1}+{\epsilon }_{t},\end{array}$`

where

• yt is an m = `numDims` dimensional time series corresponding to m response variables at time t, t = 1,...,T.

• $\Phi \left(L\right)=I-{\Phi }_{1}-{\Phi }_{2}-...-{\Phi }_{q}$, I is the m-by-m identity matrix, and Lyt = yt – 1.

• The cointegrating relations are B'yt – 1 + c0 + d0t and the error-correction term is A(B'yt – 1 + c0 + d0t).

• r is the number of cointegrating relations and, in general, 0 ≤ rm.

• A is an m-by-r matrix of adjustment speeds.

• B is an m-by-r cointegration matrix.

• C = AB′ is an m-by-m impact matrix with a rank of r.

• c0 is an r-by-1 vector of constants (intercepts) in the cointegrating relations.

• d0 is an r-by-1 vector of linear time trends in the cointegrating relations.

• c1 is an m-by-1 vector of constants (deterministic linear trends in yt).

• d1 is an m-by-1 vector of linear time-trend values (deterministic quadratic trends in yt).

• c = Ac0 + c1 and is the overall constant.

• d = Ad0 + d1 and is the overall time-trend coefficient.

• Φj is an m-by-m matrix of short-run coefficients, where j = 1,...,q and Φq is not a matrix containing only zeros.

• 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.

If m = r, then the VEC model is a stable VAR(q + 1) model in the levels of the responses. If r = 0, then the error-correction term is a matrix of zeros, and the VEC(q) model is a stable VAR(q) model in the first differences of the responses.

Tips

• `jcontest` compares finite-sample statistics to asymptotic critical values, and tests can show significant size distortions for small samples [2]. Larger samples lead to more reliable inferences.

• To convert VEC(q) model parameters in the `mles` output to vector autoregressive (VAR) model parameters, use the `vec2var` function.

Algorithms

• `jcontest` identifies deterministic terms that are outside of the cointegrating relations, c1 and d1, by projecting constant and linear regression coefficients, respectively, onto the orthogonal complement of A.

• The parameters A and B in the reduced-rank VEC(q) model are not identifiable. `jcontest` identifies B using the methods in [3], depending on the test.

• Tests on B answer questions about the space of cointegrating relations. Tests on A answer questions about common driving forces in the system. For example, an all-zero row in A indicates a variable that is weakly exogenous with respect to the coefficients in B. Such a variable can affect other variables, but it does not adjust to disequilibrium in the cointegrating relations. Similarly, a standard unit vector column in A indicates a variable that is exclusively adjusting to disequilibrium in a particular cointegrating relation.

• Constraint matrices `R` satisfying RA = 0 or RB = 0 are equivalent to A = Hφ or B = Hφ, where H is the orthogonal complement of R (`null(R')`) and φ is a vector of free parameters.

References

[1] Hamilton, James D. Time Series Analysis. Princeton, NJ: Princeton University Press, 1994.

[2] Haug, A. “Testing Linear Restrictions on Cointegrating Vectors: Sizes and Powers of Wald Tests in Finite Samples.” Econometric Theory. v. 18, 2002, pp. 505–524.

[3] Johansen, S. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford: Oxford University Press, 1995.

[4] Juselius, K. The Cointegrated VAR Model. Oxford: Oxford University Press, 2006.

[5] Morin, N. "Likelihood Ratio Tests on Cointegrating Vectors, Disequilibrium Adjustment Vectors, and their Orthogonal Complements." European Journal of Pure and Applied Mathematics. v. 3, 2010, pp. 541–571.

Version History

Introduced in R2011a