summarize
Display estimation results of vector autoregression (VAR) model
Description
summarize(
displays a summary of the VAR(p) model Mdl
)Mdl
.
If
Mdl
is an estimated VAR model returned byestimate
, thensummarize
prints estimation results to the MATLAB® Command Window. The display includes a table of parameter estimates with corresponding standard errors, t statistics, and p-values. The summary also includes the loglikelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) model fit statistics, as well as the estimated innovations covariance and correlation matrices.If
Mdl
is an unestimated VAR model returned byvarm
, thensummarize
prints the standard object display (the same display thatvarm
prints during model creation).
Examples
Fit VAR(4) Model to Matrix of Response Data
Fit a VAR(4) model to the consumer price index (CPI) and unemployment rate series. Supply the response series as a numeric matrix.
Load the Data_USEconModel
data set.
load Data_USEconModel
Plot the two series on separate plots.
figure; plot(DataTimeTable.Time,DataTimeTable.CPIAUCSL); title('Consumer Price Index') ylabel('Index') xlabel('Date')
figure; plot(DataTimeTable.Time,DataTimeTable.UNRATE); title('Unemployment Rate'); ylabel('Percent'); xlabel('Date');
Stabilize the CPI by converting it to a series of growth rates. Synchronize the two series by removing the first observation from the unemployment rate series.
rcpi = price2ret(DataTimeTable.CPIAUCSL); unrate = DataTimeTable.UNRATE(2:end);
Create a default VAR(4) model by using the shorthand syntax.
Mdl = varm(2,4)
Mdl = varm with properties: Description: "2-Dimensional VAR(4) Model" SeriesNames: "Y1" "Y2" NumSeries: 2 P: 4 Constant: [2×1 vector of NaNs] AR: {2×2 matrices of NaNs} at lags [1 2 3 ... and 1 more] Trend: [2×1 vector of zeros] Beta: [2×0 matrix] Covariance: [2×2 matrix of NaNs]
Mdl
is a varm
model object. All properties containing NaN
values correspond to parameters to be estimated given data.
Estimate the model using the entire data set.
EstMdl = estimate(Mdl,[rcpi unrate])
EstMdl = varm with properties: Description: "AR-Stationary 2-Dimensional VAR(4) Model" SeriesNames: "Y1" "Y2" NumSeries: 2 P: 4 Constant: [0.00171639 0.316255]' AR: {2×2 matrices} at lags [1 2 3 ... and 1 more] Trend: [2×1 vector of zeros] Beta: [2×0 matrix] Covariance: [2×2 matrix]
EstMdl
is an estimated varm
model object. It is fully specified because all parameters have known values. The description indicates that the autoregressive polynomial is stationary.
Display summary statistics from the estimation.
summarize(EstMdl)
AR-Stationary 2-Dimensional VAR(4) Model Effective Sample Size: 241 Number of Estimated Parameters: 18 LogLikelihood: 811.361 AIC: -1586.72 BIC: -1524 Value StandardError TStatistic PValue ___________ _____________ __________ __________ Constant(1) 0.0017164 0.0015988 1.0735 0.28303 Constant(2) 0.31626 0.091961 3.439 0.0005838 AR{1}(1,1) 0.30899 0.063356 4.877 1.0772e-06 AR{1}(2,1) -4.4834 3.6441 -1.2303 0.21857 AR{1}(1,2) -0.0031796 0.0011306 -2.8122 0.004921 AR{1}(2,2) 1.3433 0.065032 20.656 8.546e-95 AR{2}(1,1) 0.22433 0.069631 3.2217 0.0012741 AR{2}(2,1) 7.1896 4.005 1.7951 0.072631 AR{2}(1,2) 0.0012375 0.0018631 0.6642 0.50656 AR{2}(2,2) -0.26817 0.10716 -2.5025 0.012331 AR{3}(1,1) 0.35333 0.068287 5.1742 2.2887e-07 AR{3}(2,1) 1.487 3.9277 0.37858 0.705 AR{3}(1,2) 0.0028594 0.0018621 1.5355 0.12465 AR{3}(2,2) -0.22709 0.1071 -2.1202 0.033986 AR{4}(1,1) -0.047563 0.069026 -0.68906 0.49079 AR{4}(2,1) 8.6379 3.9702 2.1757 0.029579 AR{4}(1,2) -0.00096323 0.0011142 -0.86448 0.38733 AR{4}(2,2) 0.076725 0.064088 1.1972 0.23123 Innovations Covariance Matrix: 0.0000 -0.0002 -0.0002 0.1167 Innovations Correlation Matrix: 1.0000 -0.0925 -0.0925 1.0000
Compare Several VAR Model Fits
Consider these four VAR models of consumer price index (CPI) and unemployment rate: VAR(0), VAR(1), VAR(4), and VAR(8). Using historical data, estimate each, and then compare the model fits using the resulting BIC.
Load the Data_USEconModel
data set. Declare variables for the consumer price index (CPI
) and unemployment rate (UNRATE
) series. Remove any missing values from the beginning of the series.
load Data_USEconModel
cpi = DataTimeTable.CPIAUCSL;
unrate = DataTimeTable.UNRATE;
idx = all(~isnan([cpi unrate]),2);
cpi = cpi(idx);
unrate = unrate(idx);
Stabilize CPI by converting it to a series of growth rates. Synchronize the two series by removing the first observation from the unemployment rate series.
rcpi = price2ret(cpi); unrate = unrate(2:end);
Within a loop:
Create a VAR model using the shorthand syntax.
Estimate the VAR Model. Reserve the maximum value of p as presample observations.
Store the estimation results.
numseries = 2; p = [0 1 4 8]; estMdlResults = cell(numel(p),1); % Preallocation Y0 = [rcpi(1:max(p)) unrate(1:max(p))]; Y = [rcpi((max(p) + 1):end) unrate((max(p) + 1):end)]; for j = 1:numel(p) Mdl = varm(numseries,p(j)); EstMdl = estimate(Mdl,Y,'Y0',Y); estMdlResults{j} = summarize(EstMdl); end
estMdlResults
is a 4-by-1 cell array of structure arrays containing the estimation results of each model.
Extract the BIC from each set of results.
BIC = cellfun(@(x)x.BIC,estMdlResults)
BIC = 4×1
103 ×
-0.7153
-1.3678
-1.4378
-1.3853
The model corresponding to the lowest BIC has the best fit among the models considered. Therefore, the VAR(4) is the best fitting model.
Input Arguments
Output Arguments
results
— Model summary
structure array | varm
model object
Model summary, returned as a structure array or a varm
model object.
If
Mdl
is an estimated VAR model, thenresults
is a structure array containing the fields in this table.Field Description Description
Model summary description (string) SampleSize
Effective sample size (numeric scalar) NumEstimatedParameters
Number of estimated parameters (numeric scalar) LogLikelihood
Optimized loglikelihood value (numeric scalar) AIC
Akaike information criterion (numeric scalar) BIC
Bayesian information criterion (numeric scalar) Table
Parameter 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 Covariance
Estimated residual covariance matrix (the maximum likelihood estimate), a Mdl.NumSeries
-by-Mdl.NumSeries
numeric matrix with rows and columns corresponding to the innovations in the response equations ordered by the dataY
Correlation
Estimated residual correlation matrix, its dimensions correspond to the dimensions of Covariance
summarize
usesmvregress
to implement multivariate normal, maximum likelihood estimation. For more details on estimates and standard errors, see Estimation of Multivariate Regression Models.If
Mdl
is an unestimated VAR model, thenresults
is avarm
model object that is equal toMdl
.
Version History
Introduced in R2017a
MATLAB Command
You clicked a link that corresponds to this MATLAB command:
Run the command by entering it in the MATLAB Command Window. Web browsers do not support MATLAB commands.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)