Object Reference : Object View and Procedure Reference : Var
  
 
vdecomp
Variance decomposition in VARs.
Syntax
var_name.vdecomp(n, options) [response_series] [@comp component_series] [@order ordering_series]
In the syntax above, n is the number of periods over which to compute variance decompositions. When a value for n is not provided, it defaults to 10.
You may optionally filter and sort variance decomposition results (for displaying results and outputting results into the workfile) using response_series and component_series.
• response_series is a space-delimited list of series names used to filter and sort results based on the response (decomposition) series. It is set to the VAR object’s endog_list by default.
• component_series is a space-delimited list of series names used to filter and sort results based on the component series, and is preceded by the keyword “@comp”. It is set to the VAR object’s endog_list by default.
If using Cholesky factorization, you may specify the Cholesky ordering by listing the order of the series after “@order”. The default behavior is equivalent to setting ordering_series to the VAR object’s endog_list. Note that every variable that appears in the VAR object’s endog_list must be included in ordering_series.
Legacy Syntax
The following syntax has been deprecated:
var_name.vdecomp(n, options) [response_series] [@ @ ordering_series]
var_name.decomp(n, options) [response_series] [@ @ ordering_series]
While you may use the older forms of the command, mixing the current and legacy syntax is not allowed.
Options
General Options
 
g
Display combined graphs, with the decompositions for each variable shown in a graph.
m
Display multiple graphs, with each response-component pair shown in a separate graph.
t (default)
Show numerical results in table.
imp=arg (default=“chol”)
Type of factorization for the decomposition: “chol” (Cholesky with d.f. correction), “mlechol” (Cholesky without d.f. correction), “struct” (structural).
The structural factorization is based on the estimated structural VAR. To use this option, you must first estimate the structural decomposition; see Var::svar.
The option “imp=mlechol” is provided for backward compatibility with EViews 3.x and earlier.
se=mcarlo
Monte Carlo standard errors. You must specify the number of replications with the “rep=” option.
Currently available only when you have specified the Cholesky factorization (using the “imp=chol” option).
rep=integer
Number of Monte Carlo replications to be used in computing the standard errors. Must be used with the “se=mcarlo” option.
cilevels=arg (default = “0.95”)
Confidence interval coverage: space limited list of numbers between 0 and 1.
uselines
Use lines instead of shading for confidence intervals.
save=name
Save variance decomposition estimates in a named matrix.
Grouping: Results are grouped by component, unless the byrsp keyword is used in which case results are grouped by response (decomposition).
Filtering and sorting: Results are filtered and sorted according to response_series and component_series.
savese=name
Save variance decomposition standard errors in a named matrix. No output will be generated if the SE/CI method is set to ‘None’.
Grouping: Results are grouped by component, unless the byrsp keyword is used in which case results are grouped by response (decomposition).
Filtering and sorting: Results are filtered and sorted according to response_series and component_series.
saveci=name
Save variance decomposition confidence intervals in a named matrix. No output will be generated if the SE/CI method is set to ‘None’.
Grouping: Results are grouped by component, unless the byrsp keyword is used in which case results are grouped by response (decomposition).
Filtering and sorting: Results are filtered and sorted according to response_series and component_series.
saverci=name
Save variance decomposition estimates and confidence intervals in a named matrix. Estimates are interleaved. No output will be generated if the SE/CI method is set to ‘None’.
Grouping: Results are grouped by component, unless the byrsp keyword is used in which case results are grouped by response (decomposition).
Filtering and sorting: Results are filtered and sorted according to response_series and component_series.
savefse=name
Save forecast standard errors in a named matrix.
Filtering and sorting: Results are filtered and sorted according to response_series.
byrsp
Group results by response (decomposition) instead of by component.
prompt
Force the dialog to appear from within a program.
p
Print results.
Legacy Save (Output to Workfile) Options
The following save options have been deprecated and are only supported for variance decomposition methods introduced before EViews 13.
Mixing current and legacy save options is not allowed.
 
matbys=name
(1) Save variance decomposition estimates in a named matrix.
(2) Save forecast standard errors in a named matrix.
(3) Save variance decomposition standard errors in a named matrix, unless the SE/CI method is set to ‘None’.
Grouping: Results are grouped by component.
Filtering and sorting: Results are neither filtered nor sorted. User’s specifications for response_series and component_series are ignored and replaced with the VAR object’s endog_list.
matbyr=name
(1) Save variance decomposition estimates in a named matrix.
(2) Save forecast standard errors in a named matrix.
(3) Save variance decomposition standard errors in a named matrix, unless the SE/CI method is set to ‘None’.
Grouping: Results are grouped by response (decomposition).
Filtering and sorting: Results are neither filtered nor sorted. User’s specifications for response_series and component_series are ignored and replaced with the VAR object’s endog_list.
Examples
var var1.ls 1 4 m1 gdp cpi
var1.vdecomp(10,t) gdp
The first line declares and estimates a VAR with three variables and lags from 1 to 4. The second line tabulates the variance decompositions of GDP up to 10 periods using the ordering as specified in VAR1.
var1.vdecomp(10,t) gdp @order cpi gdp m1
performs the same variance decomposition as above using a different Cholesky ordering.
Cross-references
See “Variance Decomposition” for additional details.
See also Var::impulse.