Documentation

This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

summarize

Display vector error-correction (VEC) model estimation results

Syntax

summarize(Mdl)
results = summarize(Mdl)

Description

example

summarize(Mdl) displays a summary of the VEC(p – 1) model Mdl.

  • If Mdl is an estimated VEC model returned by estimate, then summarize prints estimation results to the MATLAB® Command Window. The display includes an estimation summary and a table of parameter estimates with corresponding standard errors, t statistics, and p-values. The estimation summary includes fit statistics, such as the Akaike Information Criterion (AIC), and the estimated innovations covariance and correlation matrices.

  • If Mdl is an unestimated VEC model returned by vecm, then summarize prints the standard object display (the same display that vecm prints during model creation).

example

results = summarize(Mdl) returns one of the following variables and does not print to the Command Window.

  • If Mdl is an estimated VEC model, then results is a structure containing estimation results.

  • If Mdl is an unestimated VEC model, then results is a vecm model object that is equal to Mdl.

Examples

collapse all

Consider a VEC model for the following seven macroeconomic series, and then fit the model to the data.

  • 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 [1]
                   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 [1]
                   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.

Display a short summary from the estimation.

results = summarize(EstMdl)
results = 

  struct with fields:

               Description: "7-Dimensional Rank = 4 VEC(1) Model"
                     Model: "H1"
                SampleSize: 238
    NumEstimatedParameters: 112
             LogLikelihood: -1.4939e+03
                       AIC: 3.2118e+03
                       BIC: 3.6007e+03
                     Table: [133x4 table]
                Covariance: [7x7 double]
               Correlation: [7x7 double]

The Table field of results is a table of parameter estimates and corresponding statistics.

Consider the model and data in Estimate VEC Model and these four alternative VEC models: VEC(0), VEC(1), VEC(3), and VEC(7). Using historical data, estimate each of the four models, and then compare the model fits using the resulting Bayesian Information Criterion (BIC).

Load the Data_USEconVECModel data set and preprocess the data.

load Data_USEconVECModel
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);

Within a loop:

  • Create a VEC model using the shorthand syntax.

  • Estimate the VEC Model. Reserve the maximum value of p as presample observations.

  • Store the estimation results.

numlags = [0 1 3 7];
p = numlags + 1;
Y0 = FRED{1:max(p),:};
Y = FRED{((max(p) + 1):end),:};

for j = 1:numel(p)
    Mdl = vecm(7,4,numlags(j));
    EstMdl = estimate(Mdl,Y,'Y0',Y);
    results(j) = summarize(EstMdl);
end

results is a 4-by-1 structure array containing the estimation results of each model.

Extract the BIC from each set of results.

BIC = [results.BIC]
BIC =

   1.0e+03 *

    5.3948    5.4372    5.8254    6.5536

The model corresponding to the lowest BIC has the best fit among the models considered. Therefore, the VEC(0) model is the best fitting model.

Input Arguments

collapse all

VEC model, specified as a vecm model object returned by estimate or vecm.

Output Arguments

collapse all

Model summary, returned as a structure array or a vecm model object.

  • If Mdl is an estimated VEC model, then results is a structure array containing the fields in this table.

    FieldDescription
    DescriptionModel summary description (string)
    ModelJohansen model of deterministic terms ("H2", "H1*", "H1", "H*", "H") [1]
    SampleSizeEffective sample size (numeric scalar)
    NumEstimatedParametersNumber of estimated parameters (numeric scalar)
    LogLikelihoodOptimized loglikelihood value (numeric scalar)
    AICAkaike Information Criterion (numeric scalar)
    BICBayesian Information Criterion (numeric scalar)
    TableParameter estimates with corresponding standard errors, t statistics (estimate divided by standard error), and p-values (assuming normality); a table with rows corresponding to model parameters
    CovarianceEstimated residual covariance matrix (the maximum likelihood estimate), an Mdl.NumSeries-by-Mdl.NumSeries numeric matrix with rows and columns corresponding to the innovations in the response equations ordered by the columns of Y
    CorrelationEstimated residual correlation matrix whose dimensions correspond to the dimensions of Covariance

  • If Mdl is an unestimated VEC model, then results is a vecm model object that is equal to Mdl.

References

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

See Also

Using Objects

Functions

Introduced in R2017b

Was this topic helpful?