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Block-wise Granger causality and block exogeneity tests

The `gctest`

function conducts a block-wise Granger causality test by accepting
sets of time series data representing the "cause" and "effect" multivariate response variables
in the test. `gctest`

supports the inclusion of optional endogenous
conditioning variables in the model for the test.

To conduct the leave-one-out, exclude-all, and block-wise Granger causality tests on the
response variables of a fully specified VAR model (represented by a `varm`

model object), see `gctest`

.

`h = gctest(Y1,Y2)`

`h = gctest(Y1,Y2,Y3)`

`h = gctest(___,Name,Value)`

`[h,pvalue,stat,cvalue] = gctest(___)`

returns the test decision `h`

= gctest(`Y1`

,`Y2`

)`h`

from conducting a block-wise Granger causality test for
assessing whether a set of time series variables `Y1`

Granger-causes a
distinct set of the time series variables `Y2`

. The
`gctest`

function conducts tests in the vector autoregression (VAR)
framework and treats `Y1`

and `Y2`

as response
(endogenous) variables during testing.

specifies options using one or more name-value pair arguments in addition to the input
argument combinations in previous syntaxes. For example,
`h`

= gctest(___,`Name,Value`

)`'Test',"f",'NumLags',2`

specifies conducting an *F*
test that compares the residual sum of squares between restricted and unrestricted VAR(2)
models for all response variables.

Conduct a Granger causality test to assess whether the M1 money supply has an impact on the predictive distribution of the consumer price index (CPI).

Load the US macroeconomic data set `Data_USEconModel.mat`

.

`load Data_USEconModel`

The data set includes the MATLAB® timetable `DataTable`

, which contains 14 variables measured from Q1 1947 through Q1 2009. `M1SL`

is the table variable containing the M1 money supply, and `CPIAUCSL`

is the table variable containing the CPI. For more details, enter `Description`

at the command line.

Visually assess whether the series are stationary by plotting them in the same figure.

figure; yyaxis left plot(DataTable.Time,DataTable.CPIAUCSL) ylabel("CPI"); yyaxis right plot(DataTable.Time,DataTable.M1SL); ylabel("Money Supply");

Both series are nonstationary.

Stabilize the series by converting them to rates.

m1slrate = price2ret(DataTable.M1SL); inflation = price2ret(DataTable.CPIAUCSL);

Assume that a VAR(1) model is an appropriate multivariate model for the rates. Conduct a default Granger causality test to assess whether the M1 money supply rate Granger-causes the inflation rate.

h = gctest(m1slrate,inflation)

`h = `*logical*
1

The test decision `h`

is `1`

, which indicates the rejection of the null hypothesis that the M1 money supply rate does not Granger-cause inflation.

Time series undergo feedback when they Granger-cause each other. Assess whether the US inflation and M1 money supply rates undergo feedback.

Load the US macroeconomic data set `Data_USEconModel.mat`

. Convert the price series to returns.

```
load Data_USEconModel
inflation = price2ret(DataTable.CPIAUCSL);
m1slrate = price2ret(DataTable.M1SL);
```

Conduct a Granger causality test to assess whether the inflation rate Granger-causes the M1 money supply rate. Assume that an underlying VAR(1) model is appropriate for the two series. The default level of significance $\alpha $ for the test is 0.05. Because this example conducts two tests, decrease $\alpha \text{\hspace{0.17em}}$ by half for each test to achieve a family-wise level of significance of 0.05.

`hIRgcM1 = gctest(inflation,m1slrate,"Alpha",0.025)`

`hIRgcM1 = `*logical*
1

The test decision `hIRgcM1`

= `1`

indicates rejection of the null hypothesis of noncausality. There is enough evidence to suggest that the inflation rate Granger-causes the M1 money supply rate at 0.025 level of significance.

Conduct another Granger causality test to assess whether the M1 money supply rate Granger-causes the inflation rate.

`hM1gcIR = gctest(m1slrate,inflation,"Alpha",0.025)`

`hM1gcIR = `*logical*
0

The test decision `hM1gcIR`

= `0`

indicates that the null hypothesis of noncausality should not be rejected. There is not enough evidence to suggest that the M1 money supply rate Granger-causes the inflation rate at 0.025 level of significance.

Because not enough evidence exists to suggest that the inflation rate Granger-causes the M1 money supply rate, the two series do not undergo feedback.

Assess whether the US gross domestic product (GDP) Granger-causes CPI conditioned on the M1 money supply.

Load the US macroeconomic data set `Data_USEconModel.mat`

.

`load Data_USEconModel`

The variables `GDP`

and `GDPDEF`

of `DataTable`

are the US GDP and its deflator with respect to year 2000 dollars, respectively. Both series are nonstationary.

Convert the M1 money supply and CPI to rates. Convert the US GDP to the real GDP rate.

m1slrate = price2ret(DataTable.M1SL); inflation = price2ret(DataTable.CPIAUCSL); rgdprate = price2ret(DataTable.GDP./DataTable.GDPDEF);

Assume that a VAR(1) model is an appropriate multivariate model for the rates. Conduct a Granger causality test to assess whether the real GDP rate has an impact on the predictive distribution of the inflation rate, conditioned on the M1 money supply. The inclusion of a conditional variable forces `gctest`

to conduct a 1-step Granger causality test.

h = gctest(rgdprate,inflation,m1slrate)

`h = `*logical*
0

The test decision `h`

is `0`

, which indicates failure to reject the null hypothesis that the real GDP rate is not a 1-step Granger-cause of inflation when you account for the M1 money supply rate.

`gctest`

includes the M1 money supply rate as a response variable in the underlying VAR(1) model, but it does not include the M1 money supply in the computation of the test statistics.

Conduct the test again, but without conditioning on the M1 money supply rate.

h = gctest(rgdprate,inflation)

`h = `*logical*
0

The test result is the same as before, suggesting that the real GDP rate does not Granger-cause inflation at all periods in a forecast horizon and regardless of whether you account for the M1 money supply rate in the underlying VAR(1) model.

By default, `gctest`

assumes an underlying VAR(1) model for all specified response variables. However, a VAR(1) model might be an inappropriate representation of the data. For example, the model might not capture all the serial correlation present in the variables.

To specify a more complex underlying VAR model, you can increase the number of lags by specifying the `'NumLags'`

name-value pair argument of `gctest`

.

Consider the Granger causality tests conducted in Conduct 1-Step Granger Causality Test Conditioned on Variable. Load the US macroeconomic data set `Data_USEconModel.mat`

. Convert the M1 money supply and CPI to rates. Convert the US GDP to the real GDP rate.

```
load Data_USEconModel
m1slrate = price2ret(DataTable.M1SL);
inflation = price2ret(DataTable.CPIAUCSL);
rgdprate = price2ret(DataTable.GDP./DataTable.GDPDEF);
```

Preprocess the data by removing all missing observations (indicated by `NaN`

).

```
idx = sum(isnan([m1slrate inflation rgdprate]),2) < 1;
m1slrate = m1slrate(idx);
inflation = inflation(idx);
rgdprate = rgdprate(idx);
T = numel(m1slrate); % Total sample size
```

Fit VAR models, with lags ranging from 1 to 4, to the real GDP and inflation rate series. Initialize each fit by specifying the first four observations. Store the Akaike information criteria (AIC) of the fits.

numseries = 2; numlags = (1:4)'; nummdls = numel(numlags); % Partition time base. maxp = max(numlags); % Maximum number of required presample responses idxpre = 1:maxp; idxest = (maxp + 1):T; % Preallocation EstMdl(nummdls) = varm(numseries,0); aic = zeros(nummdls,1); % Fit VAR models to data. Y0 = [rgdprate(idxpre) inflation(idxpre)]; % Presample Y = [rgdprate(idxest) inflation(idxest)]; % Estimation sample for j = 1:numel(numlags) Mdl = varm(numseries,numlags(j)); Mdl.SeriesNames = ["rGDP" "Inflation"]; EstMdl(j) = estimate(Mdl,Y,'Y0',Y0); results = summarize(EstMdl(j)); aic(j) = results.AIC; end p = numlags(aic == min(aic))

p = 3

A VAR(3) model yields the best fit.

Assess whether the real GDP rate Granger-causes inflation. `gctest`

removes $\mathit{p}$ observations from the beginning of the input data to initialize the underlying VAR($\mathit{p}$) model for estimation. Prepend only the required $\mathit{p}$ = 3 presample observations to the estimation sample. Specify the concatenated series as input data. Return the $\mathit{p}$-value of the test.

```
rgdprate3 = [Y0((end - p + 1):end,1); Y(:,1)];
inflation3 = [Y0((end - p + 1):end,2); Y(:,2)];
[h,pvalue] = gctest(rgdprate3,inflation3,"NumLags",p)
```

`h = `*logical*
1

pvalue = 7.7741e-04

The $\mathit{p}$-value is approximately `0.0008,`

indicating the existence of strong evidence to reject the null hypothesis of noncausality, that is, that the three real GDP rate lags in the inflation rate equation are jointly zero. Given the VAR(3) model, there is enough evidence to suggest that the real GDP rate Granger-causes at least one future value of the inflation rate.

Alternatively, you can conduct the same test by passing the estimated VAR(3) model (represented by the `varm`

model object in `EstMdl(3)`

), to the object function `gctest`

. Specify a block-wise test and the "cause" and "effect" series names.

h = gctest(EstMdl(3),'Type',"block-wise",... 'Cause',"rGDP",'Effect',"Inflation")

H0 Decision Distribution Statistic PValue CriticalValue ____________________________________________ ___________ ____________ _________ __________ _____________ "Exclude lagged rGDP in Inflation equations" "Reject H0" "Chi2(3)" 16.799 0.00077741 7.8147

`h = `*logical*
1

If you are testing integrated series for Granger causality, then the Wald test statistic does not follow a ${\chi}^{2}$ or $\mathit{F}$ distribution, and test results can be unreliable. However, you can implement the Granger causality test in [5] by specifying the maximum integration order among all the variables in the system using the `'Integration'`

name-value pair argument.

Consider the Granger causality tests conducted in Conduct 1-Step Granger Causality Test Conditioned on Variable. Load the US macroeconomic data set `Data_USEconModel.mat`

and take the log of real GDP and CPI.

```
load Data_USEconModel
cpi = log(DataTable.CPIAUCSL);
rgdp = log(DataTable.GDP./DataTable.GDPDEF);
```

Assess whether the real GDP Granger-causes CPI. Assume the series are $\mathit{I}\left(1\right)$, or order-1 integrated. Also, specify an underlying VAR(3) model and the $\mathit{F}$ test. Return the test statistic and $\mathit{p}$-value.

[h,pvalue,stat] = gctest(rgdp,cpi,'NumLags',3,... 'Integration',1,'Test',"f")

`h = `*logical*
1

pvalue = 0.0031

stat = 4.7557

The $\mathit{p}$-value = `0.0031`

, indicating the existence of strong evidence to reject the null hypothesis of noncausality, that is, that the three real GDP lags in the CPI equation are jointly zero. Given the VAR(3) model, there is enough evidence to suggest that real GDP Granger-causes at least one future value of the CPI.

In this case, the test augments the VAR(3) model with an additional lag. In other words, the model is a VAR(4) model. However, `gctest`

tests only whether the first three lags are 0.

Time series are *block exogenous* if they do not Granger-cause any other variables in a multivariate system. Test whether the effective federal funds rate is block exogenous with respect to the real GDP, personal consumption expenditures, and inflation rates.

Load the US macroeconomic data set `Data_USEconModel.mat`

. Convert the price series to returns.

```
load Data_USEconModel
inflation = price2ret(DataTable.CPIAUCSL);
rgdprate = price2ret(DataTable.GDP./DataTable.GDPDEF);
pcerate = price2ret(DataTable.PCEC);
```

Test whether the federal funds rate is nonstationary by conducting an augmented Dickey-Fuller test. Specify that the alternative model has a drift term and an $\mathit{F}$ test.

h = adftest(DataTable.FEDFUNDS,'Model',"ard")

`h = `*logical*
0

The test decision `h`

= `0`

indicates that the null hypothesis that the series has a unit root should not be rejected.

To stabilize the federal funds rate series, apply the first difference to it.

dfedfunds = diff(DataTable.FEDFUNDS);

Assume a 4-D VAR(3) model for the four series. Assess whether the federal funds rate is block exogenous with respect to the real GDP, personal consumption expenditures, and inflation rates. Conduct an $\mathit{F}$-based Wald test, and return the $\mathit{p}$-value, test statistic, and critical value.

cause = dfedfunds; effects = [inflation rgdprate pcerate]; [hgc,pvalue,stat,cvalue] = gctest(cause,effects,'NumLags',2,... 'Test',"f")

`hgc = `*logical*
1

pvalue = 4.1619e-10

stat = 10.4383

cvalue = 2.1426

The test decision `hgc`

= `1`

indicates that the null hypothesis that the federal funds rate is block exogenous should be rejected. This result suggests that the federal funds rate Granger-causes at least one of the other variables in the system.

To determine which variables the federal funds rate Granger-causes, you can run a leave-one-out test. For more details, see `gctest`

.

`Y1`

— Data for response variables representing Granger-causesnumeric vector | numeric matrix

Data for the response variables representing the Granger-causes in the test,
specified as a `numobs1`

-by-1 numeric vector or a
`numobs1`

-by-`numseries1`

numeric matrix.
`numobs1`

is the number of observations and
`numseries1`

is the number of time series variables.

Row *t* contains the observation in time *t*, the
last row contains the latest observation. `Y1`

must have enough rows to
initialize and estimate the underlying VAR model. `gctest`

uses
the first `NumLags`

observations to initialize the model for
estimation.

Columns correspond to distinct time series variables.

**Data Types: **`double`

| `single`

`Y2`

— Data for response variables affected by Granger-causesnumeric vector | numeric matrix

Data for response variables affected by the Granger-causes in the test, specified as
a `numobs2`

-by-1 numeric vector or a
`numobs2`

-by-`numseries2`

numeric matrix.
`numobs2`

is the number of observations in the data and
`numseries2`

is the number of time series variables.

Row *t* contains the observation in time *t*, the
last row contains the latest observation. `Y2`

must have enough rows to
initialize and estimate the underlying VAR model. `gctest`

uses
the first `NumLags`

observations to initialize the model for
estimation.

Columns correspond to distinct time series variables.

**Data Types: **`double`

| `single`

`Y3`

— Data for conditioning response variablesnumeric vector | numeric matrix

Data for conditioning response variables, specified as a
`numobs3`

-by-1 numeric vector or a
`numobs3`

-by-`numseries3`

numeric matrix.
`numobs3`

is the number of observations in the data and
`numseries3`

is the number of time series variables.

Row *t* contains the observation in time *t*, the
last row contains the latest observation. `Y3`

must have enough rows to
initialize and estimate the underlying VAR model. `gctest`

uses
the first `NumLags`

observations to initialize the model for
estimation.

Columns correspond to distinct time series variables.

If you specify `Y3`

, then `Y1`

,
`Y2`

, and `Y3`

represent the response variables in
the underlying VAR model. `gctest`

assesses whether
`Y1`

is a 1-step Granger-cause of `Y2`

.

**Data Types: **`double`

| `single`

Specify optional
comma-separated pairs of `Name,Value`

arguments. `Name`

is
the argument name and `Value`

is the corresponding value.
`Name`

must appear inside quotes. You can specify several name and value
pair arguments in any order as
`Name1,Value1,...,NameN,ValueN`

.

`'Alpha',0.10,'NumLags',2`

specifies a `0.10`

significance level for the test and uses an underlying VAR(2) model for all response
variables.`'NumLags'`

— Number of lagged responses`1`

(default) | nonnegative integerNumber of lagged responses to include in the underlying VAR model for all response
variables, specified as the comma-separated pair consisting of
`'NumLags'`

and a nonnegative integer. The resulting underlying
model is a VAR(`NumLags`

) model.

**Example: **`'NumLags',2`

**Data Types: **`double`

| `single`

`'Integration'`

— Maximum order of integration`0`

(default) | nonnegative integerMaximum order of integration among all response variables, specified as the
comma-separated pair consisting of `'Integration'`

and a nonnegative
integer.

To address integration, `gctest`

augments the
VAR(`NumLags`

) model by adding additional lagged responses beyond
`NumLags`

to all equations during estimation. For more details, see
[5] and [3].

**Example: **`'Integration',1`

**Data Types: **`double`

| `single`

`'Constant'`

— Flag indicating inclusion of model intercepts`true`

(default) | `false`

Flag indicating the inclusion of model intercepts (constants) in the underlying
VAR model, specified as the comma-separated pair consisting of
`'Constant'`

and a value in this table.

Value | Description |
---|---|

`true` | All equations in the underlying VAR model have an intercept.
`gctest` estimates the intercepts with all other
estimable parameters. |

`false` | All underlying VAR model equations do not have an intercept.
`gctest` sets all intercepts to 0. |

**Example: **`'Constant',false`

**Data Types: **`logical`

`'Trend'`

— Flag indicating inclusion of linear time trends`false`

(default) | `true`

Flag indicating the inclusion of linear time trends in the underlying VAR model,
specified as the comma-separated pair consisting of `'Trend'`

and a
value in this table.

Value | Description |
---|---|

`true` | All equations in the underlying VAR model have a linear time trend.
`gctest` estimates the linear time trend
coefficients with all other estimable parameters. |

`false` | All underlying VAR model equations do not have a linear time trend. |

**Example: **`'Trend',false`

**Data Types: **`logical`

`'X'`

— Predictor datanumeric matrix

Predictor data for the regression component in the underlying VAR model, specified
as the comma-separated pair consisting of `'X'`

and a numeric matrix
containing `numpreds`

columns. `numpreds`

is the
number of predictor variables.

Row *t* contains the observation in time *t*,
and the last row contains the latest observation. `gctest`

does not use the regression component in the presample period. `X`

must have at least as many observations as the number of observations used by
`gctest`

after the presample period. Specifically,
`X`

must have at least `numobs`

–
`Mdl.P`

observations, where `numobs`

=
`min([numobs1 numobs2 numobs3])`

. If you supply more rows than
necessary, `gctest`

uses the latest observations only.

Columns correspond to individual predictor variables.
`gctest`

treats predictors as exogenous. All predictor
variables are present in the regression component of each response equation.

By default, `gctest`

excludes a regression component from
all equations.

**Data Types: **`double`

| `single`

`'Alpha'`

— Significance level`0.05`

(default) | numeric scalar in (0,1)Significance level for the test, specified as the comma-separated pair consisting
of `'Alpha'`

and a numeric scalar in (0,1).

**Example: **`'Alpha',0.1`

**Data Types: **`double`

| `single`

`'Test'`

— Test statistic distribution under the null hypothesis`"chi-square"`

(default) | `"f"`

Test statistic distribution under the null hypothesis, specified as the
comma-separated pair consisting of `'Test'`

and a value in this
table.

Value | Description |
---|---|

`"chi-square"` | `gctest` derives outputs from conducting a χ^{2} test. |

`"f"` | `gctest` derives outputs from conducting an F test. |

For test statistic forms, see [4].

**Example: **`'Test',"f"`

**Data Types: **`char`

| `string`

`h`

— Block-wise Granger causality test decisionlogical scalar

Block-wise Granger causality test decision, returned as a logical scalar.

`h`

=`1`

indicates rejection of*H*_{0}.If you specify the conditioning response data

`Y3`

, then sufficient evidence exists to suggest that the response variables represented in`Y1`

are 1-step Granger-causes of the response variables represented in`Y2`

, conditioned on the response variables represented in`Y3`

.Otherwise, sufficient evidence exists to suggest that the variables in

`Y1`

are*h*-step Granger-causes of the variables in`Y2`

for some*h*≥ 0. In other words,`Y1`

is block endogenous with respect to`Y2`

.

`h`

=`0`

indicates failure to reject*H*_{0}.If you specify

`Y3`

, then the variables in`Y1`

are not 1-step Granger-causes of the variables in`Y2`

, conditioned on`Y3`

.Otherwise,

`Y1`

does not Granger-cause`Y2`

. In other words, there is not enough evidence to reject block exogeneity of`Y1`

with respect to`Y2`

.

`pvalue`

— numeric scalar

*p*-value, returned as a numeric scalar.

`stat`

— Test statisticnumeric scalar

Test statistic, returned as a numeric scalar.

`cvalue`

— Critical valuenumeric scalar

Critical value for the significance level `Alpha`

, returned as a
numeric scalar.

The *Granger causality test* is a statistical
hypothesis test that assesses whether past and present values of a set of
*m*_{1} = `numseries1`

time series
variables *y*_{1,t}, called the
"cause" variables, affect the predictive distribution of a distinct set of
*m*_{2} = `numseries2`

time series
variables *y*_{2,t}, called the
"effect" variables. The impact is a reduction in forecast mean squared error (MSE) of
*y*_{2,t}. If past values of
*y*_{1,t} affect
*y*_{2,t + h},
then *y*_{1,t} is an
*h*-step *Granger-cause* of
*y*_{2,t}. In other words,
*y*_{1,t}
*Granger-causes*
*y*_{2,t} if
*y*_{1,t} is an
*h*-step *Granger-cause* of
*y*_{2,t} for all
*h* ≥ 1.

Consider a stationary VAR(*p*)
model for [*y*_{1,t}
*y*_{2,t}]:

$$\left[\begin{array}{c}{y}_{1,t}\\ {y}_{2,t}\end{array}\right]=c+\delta t+\beta {x}_{t}+\left[\begin{array}{cc}{\Phi}_{11,1}& {\Phi}_{12,1}\\ {\Phi}_{21,1}& {\Phi}_{22,1}\end{array}\right]\left[\begin{array}{c}{y}_{1,t-1}\\ {y}_{2,t-1}\end{array}\right]+\mathrm{...}+\left[\begin{array}{cc}{\Phi}_{11,p}& {\Phi}_{12,p}\\ {\Phi}_{21,p}& {\Phi}_{22,p}\end{array}\right]\left[\begin{array}{c}{y}_{1,t-p}\\ {y}_{2,t-p}\end{array}\right]+\left[\begin{array}{c}{\epsilon}_{1,t}\\ {\epsilon}_{2,t}\end{array}\right].$$

Assume the following conditions:

Future values cannot inform past values.

*y*_{1,t}uniquely informs*y*_{2,t}(no other variable has the information to inform*y*_{2,t}).

If Φ_{21,1} = … =
Φ_{21,p} =
0_{m1,m2},
then *y*_{1,t} is not the
*block-wise* Granger cause of
*y*_{2,t +
h}, for all *h* ≥ 1 and where
0_{m2,m1}
is an
*m*_{2}-by-*m*_{1}
matrix of zeros. Also, *y*_{1,t} is
block exogenous with respect to
*y*_{2,t}. Consequently, the
block-wise Granger causality test hypotheses are:

$$\begin{array}{l}{H}_{0}:{\Phi}_{21,1}=\mathrm{...}={\Phi}_{21,p}={0}_{{m}_{2},{m}_{1}}\\ {H}_{1}:\exists j\in \{1,\mathrm{...},p\}\ni {\Phi}_{21,j}\ne {0}_{{m}_{2},{m}_{1}}.\end{array}$$

*H*_{1} implies that at least one *h*
≥ 1 exists such that *y*_{1,t} is an
*h*-step *Granger-cause* of
*y*_{2,t}.

`gctest`

conducts
*χ*^{2}-based or *F*-based Wald
tests (see `'Test'`

). For test
statistic forms, see [4].

Distinct *conditioning* endogenous variables
*y*_{3,t} can be included in the
system (see `Y3`

). In this case, the VAR(*p*) model is:

$$\left[\begin{array}{c}{y}_{1,t}\\ \begin{array}{l}{y}_{2,t}\\ {y}_{3,t}\end{array}\end{array}\right]=c+\delta t+\beta {x}_{t}+\left[\begin{array}{ccc}{\Phi}_{11,1}& {\Phi}_{12,1}& {\Phi}_{13,1}\\ {\Phi}_{21,1}& {\Phi}_{22,1}& {\Phi}_{23,1}\\ {\Phi}_{31,1}& {\Phi}_{32,1}& {\Phi}_{33,1}\end{array}\right]\left[\begin{array}{c}{y}_{1,t-1}\\ \begin{array}{l}{y}_{2,t-1}\\ {y}_{3,t-1}\end{array}\end{array}\right]+\mathrm{...}+\left[\begin{array}{ccc}{\Phi}_{11,p}& {\Phi}_{12,p}& {\Phi}_{13,p}\\ {\Phi}_{21,p}& {\Phi}_{22,p}& {\Phi}_{23,p}\\ {\Phi}_{31,p}& {\Phi}_{32,p}& {\Phi}_{33,p}\end{array}\right]\left[\begin{array}{c}{y}_{1,t-p}\\ \begin{array}{l}{y}_{2,t-p}\\ {y}_{3,t-p}\end{array}\end{array}\right]+\left[\begin{array}{c}{\epsilon}_{1,t}\\ \begin{array}{l}{\epsilon}_{2,t}\\ {\epsilon}_{3,t}\end{array}\end{array}\right].$$

`gctest`

does not test the parameters associated
with the conditioning variables. The test assesses only whether
*y*_{1,t} is an 1-step
Granger-cause of *y*_{2,t}.

A *vector autoregression (VAR) model* is a
stationary multivariate time series model consisting of a system of *m*
equations of *m* distinct response variables as linear functions of lagged
responses and other terms.

A VAR(*p*) model in *difference-equation notation*
and in *reduced form* is

$${y}_{t}=c+{\Phi}_{1}{y}_{t-1}+{\Phi}_{2}{y}_{t-2}+\mathrm{...}+{\Phi}_{p}{y}_{t-p}+\beta {x}_{t}+\delta t+{\epsilon}_{t}.$$

*y*is a_{t}`numseries`

-by-1 vector of values corresponding to`numseries`

response variables at time*t*, where*t*= 1,...,*T*. The structural coefficient is the identity matrix.*c*is a`numseries`

-by-1 vector of constants.Φ

_{j}is a`numseries`

-by-`numseries`

matrix of autoregressive coefficients, where*j*= 1,...,*p*and Φ_{p}is not a matrix containing only zeros.*x*is a_{t}`numpreds`

-by-1 vector of values corresponding to`numpreds`

exogenous predictor variables.*β*is a`numseries`

-by-`numpreds`

matrix of regression coefficients.*δ*is a`numseries`

-by-1 vector of linear time-trend values.*ε*is a_{t}`numseries`

-by-1 vector of random Gaussian innovations, each with a mean of 0 and collectively a`numseries`

-by-`numseries`

covariance matrix Σ. For*t*≠*s*,*ε*and_{t}*ε*are independent._{s}

Condensed and in lag operator notation, the system is

$$\Phi (L){y}_{t}=c+\beta {x}_{t}+\delta t+{\epsilon}_{t},$$

where $$\Phi (L)=I-{\Phi}_{1}L-{\Phi}_{2}{L}^{2}-\mathrm{...}-{\Phi}_{p}{L}^{p}$$, Φ(*L*)*y _{t}* is
the multivariate autoregressive polynomial, and

`numseries`

-by-`numseries`

identity matrix.For example, a VAR(1) model containing two response series and three exogenous predictor variables has this form:

$$\begin{array}{l}{y}_{1,t}={c}_{1}+{\varphi}_{11}{y}_{1,t-1}+{\varphi}_{12}{y}_{2,t-1}+{\beta}_{11}{x}_{1,t}+{\beta}_{12}{x}_{2,t}+{\beta}_{13}{x}_{3,t}+{\delta}_{1}t+{\epsilon}_{1,t}\\ {y}_{2,t}={c}_{2}+{\varphi}_{21}{y}_{1,t-1}+{\varphi}_{22}{y}_{2,t-1}+{\beta}_{21}{x}_{1,t}+{\beta}_{22}{x}_{2,t}+{\beta}_{23}{x}_{3,t}+{\delta}_{2}t+{\epsilon}_{2,t}.\end{array}$$

[1]
Granger, C. W. J. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods." *Econometrica*. Vol. 37, 1969, pp. 424–459.

[2]
Hamilton, J. D. *Time Series Analysis*. Princeton, NJ: Princeton University Press, 1994.

[3]
Dolado, J. J., and H. Lütkepohl. "Making Wald Tests Work for Cointegrated VAR Systems." *Econometric Reviews*. Vol. 15, 1996, pp. 369–386.

[4]
Lütkepohl, H. *New Introduction to Multiple Time Series Analysis*. New York, NY: Springer-Verlag, 2007.

[5]
Toda, H. Y., and T. Yamamoto. "Statistical Inferences in Vector Autoregressions with Possibly Integrated Processes." *Journal of Econometrics*. Vol. 66, 1995, pp. 225–250.

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