Display impulse response functions of var object with an estimated VAR or VEC.
Syntax
var_name.impulse(n, options) [response_series] [@imp impulse_series] [@order ordering_series]
In the syntax above, n is the number of periods over which to compute impulse response functions. When a value for n is not provided, it defaults to 10.
You may optionally filter and sort impulse-response results (for displaying results and outputting results into the workfile) using response_series and impulse_series.
• response_series is a space-delimited list of series names used to filter and sort results based on the response series. It is set to the VAR object’s endog_list by default.
• impulse_series is a space-delimited list of series names used to filter and sort results based on the impulse (shock) series, and is preceded by the keyword “@imp”. It is set to the VAR object’s endog_list by default.
If you are using impulses based on the Cholesky factor, 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.impulse(n, options) [response_series] [@ impulse_series [@ ordering_series]]
Mixing the current and legacy syntax is not allowed.
Options
General Options
g | Display combined graphs, with impulse responses of one variable to all shocks shown in one graph. |
m (default) | Display multiple graphs, with impulse response to each shock shown in separate graphs. |
t | Tabulate the impulse responses. |
a | Accumulate the impulse responses. |
imp=arg (default=“chol”) | Type of factorization for the decomposition: unit impulses, ignoring correlations among the residuals (“imp=unit”), non-orthogonal, ignoring correlations among the residuals (“imp=nonort”), Cholesky with d.f. correction (“imp=chol”), Cholesky without d.f. correction (“imp=mlechol”), Generalized (“imp=gen”), structural (“imp=struct”), or user specified (“imp=user”). The structural factorization is based on the estimated structural VAR. To use this option, you must first estimate the structural decomposition; see
Var::svar. For user-specified impulses, you must specify the name of the vector/matrix containing the impulses using the “fname=” option. The option “imp=mlechol” is provided for backward compatibility with EViews 3.x and earlier. |
fname=name | Specify name of vector/matrix containing the impulses. The vector/matrix must have  rows and 1 or  columns, where  is the number of endogenous variables. |
irtype=arg (default=“ordinary”) | Type of impulse response calculation engine: “ordinary” (classical VAR), “lp” (local projection: sequential), and “lpjoint” (local projection: joint). |
se=arg | Standard error calculations: “se=a” (analytic), “se=mc” (Monte Carlo), “se=boot” (bootstrap). If selecting Monte Carlo or bootstrap, you must specify the number of replications with the “rep=” option. Bootstrap type is set with the “bs=” option. Note the following: (1) Analytic standard errors are currently not available for (a) VECs and (b) structural decompositions identified by long-run restrictions. The “se=a” option will be ignored for these cases. (2) Monte Carlo standard errors are currently not available for (a) VECs and (b) structural decompositions. The “se=mc” option will be ignored for these cases. |
rep=integer | Number of Monte Carlo and bootstrap replications to be used in computing standard errors. Must be used with the “se=mc” and “se=boot” options. |
bs=arg (default = “hp”) | Bootstrap method: “sp” (standard percentile), “hp” (Hall’s percentile), “hs” (Hall’s studentized), “ku” (Killian’s unbiased). Note the following: Hall’s studentized and Kilian’s unbiased bootstraps require a double bootstrap. They can be computed quickly using a fast double bootstrap approximation by supplying an additional “fdb” option. |
dbsrep=integer (default = 499) | Number of double bootstrap replications. Must be used with the “bs=hs” and “bs=ku” options. Note the following: This option is not necessary if supplying the “fdb” option. |
fdb | Approximate double bootstrap routines with fast double bootstrap routines. |
cilevels=arg (default = “0.95”) | Confidence interval coverage as a number between 0 and 1. Note the following: (1) Option is only available when “se=boot”. (2) Multiple confidence levels can be supplied as a space delimited list. |
uselines | Use lines instead of shading for confidence intervals. |
save=name | Save impulse-response estimates in a named matrix. Grouping: Results are grouped by impulse (shock), unless the byrsp keyword is used in which case results are grouped by response. Filtering and sorting: Results are filtered and sorted according to response_series and impulse_series. |
savese=name | Save impulse-response standard errors (or standard deviations) in a named matrix. No output will be generated if the SE/CI method is set to ‘None’. Grouping: Results are grouped by impulse (shock), unless the byrsp keyword is used in which case results are grouped by response. Filtering and sorting: Results are filtered and sorted according to response_series and impulse_series. |
saveci=name | Save impulse-response confidence intervals (or credibility intervals) in a named matrix. No output will be generated if the SE/CI method is set to ‘None’. Grouping: Results are grouped by impulse (shock), unless the byrsp keyword is used in which case results are grouped by response. Filtering and sorting: Results are filtered and sorted according to response_series and impulse_series. |
saverci=name | Save impulse-response estimates and confidence intervals (or credibility 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 impulse (shock), unless the byrsp keyword is used in which case results are grouped by response. Filtering and sorting: Results are filtered and sorted according to response_series and impulse_series. |
byrsp | Group results by response instead of by impulse (shock). |
prompt | Force the dialog to appear from within a program. |
p | Print the results. |
BVAR Options
bvartype = arg (default= “bayes”) | Impulse method: Bayesian sampling (“bayes”), classical impulse response analysis using the posterior residual covariance matrix (“classpost”), classical impulse response analysis using the empirical residual covariance matrix (“classemp”). |
If you are using Bayesian sampling, the following Bayesian options are available:
draws=integer (default= 100000) | Number of draws. |
burn=arg (default=0.1) | Proportion of initial draws to discard. |
seed=integer | Random number seed. |
dropunstable | Drop any draws that produce unstable coefficients. |
dgraph | Produce distribution graphs. |
page=arg | Store the individual draws in a new page. |
BTVCVAR Options
dates=arg | Set the impulse date(s). For multiple dates, enter a space-delimited list of values surrounded by quotation marks, e.g., “1980q1 2000q1 2020q1”. |
horizons=arg | Set the elapsed time horizon(s). For multiple horizons, enter a space-delimited list of positive integers surrounded by quotation marks, e.g., “5 9 13”. |
usemean | Use posterior mean as the point estimate. The posterior median is used if usemean is not included in the options list. |
showci | Show credibility intervals (bands). |
Local Projection Options
lpband=arg (default=“marginal”) | Standard error band type: “marginal”, “scheffe”, and “conditional”. Note that marginal error bands do not account for cross-impulse effects, whereas Scheffé and conditional do. |
asym=arg | Asymmetric/nonlinear effects specified as a categorical variable series name or expression. |
Legacy Save (Output to Workfile) Options
The following save options have been deprecated and are only supported for impulse response methods introduced before EViews 13.
Mixing current and legacy save options is not allowed.
matbys=name | (1) Save impulse-response estimates in a named matrix. (2) Save impulse-response standard errors in a named matrix, unless the SE/CI method is set to ‘None’. Grouping: Results are grouped by impulse (shock). Filtering and sorting: Results are neither filtered nor sorted. User’s specifications for response_series and impulse_series are ignored and replaced with the VAR object’s endog_list. |
matbyr=name | (1) Save impulse-response estimates in a named matrix. (2) Save impulse-response standard errors in a named matrix, unless the SE/CI method is set to ‘None’. Grouping: Results are grouped by response. Filtering and sorting: Results are neither filtered nor sorted. User’s specifications for response_series and impulse_series are ignored and replaced with the VAR object’s endog_list. |
smat=name | (1) Save impulse-response estimates in a named matrix. (2) Save impulse-response standard errors in a named matrix, unless the SE/CI method is set to ‘None’. Grouping: Results are grouped by impulse (shock). Filtering and sorting: Results are filtered and sorted according to response_series and impulse_series. |
rmat=name | (1) Save impulse-response estimates in a named matrix. (2) Save impulse-response standard errors in a named matrix, unless the SE/CI method is set to ‘None’. Grouping: Results are grouped by response. Filtering and sorting: Results are filtered and sorted according to response_series and impulse_series. |
cimat=name | (1) Save impulse-response estimates in a named matrix. (2) Save impulse-response confidence intervals in a named matrix, unless the SE/CI method is set to ‘None’. Grouping: Results are grouped by impulse (shock). Filtering and sorting: Results are filtered and sorted according to response_series and impulse_series. |
rcimat=name | (1) Save impulse-response estimates in a named matrix. (2) Save impulse response estimates and confidence intervals in a named matrix, unless the SE/CI method is set to ‘None’. Estimates are interleaved. Grouping: Results are grouped by impulse (shock). Filtering and sorting: Results are filtered and sorted according to response_series and impulse_series. |
Examples
var var1.ls 1 4 m1 gdp cpi
var1.impulse(10,m) gdp
The first line declares and estimates a VAR with three variables. The second line displays multiple graphs of the impulse responses of GDP.
var1.impulse(10,m) gdp @imp m1 @order cpi gdp m1
displays the impulse response of GDP to a one standard deviation shock in M1 using a different ordering.
var.impulse(irtype=lp)
Produces impulse response output for a VAR object called “var” using the sequential local projection impulse response engine.
var.impulse(irtype=lpjoint, asym=@month=6, lpband=scheffe)
Produces impulse response output for a VAR object called “var” using the joint local projection impulse response engine. The standard error band type is set to Scheffé and the asymmetric effect is set to a dummy variable for the month of June. In addition to standard error bands, asymmetric effects are also displayed as two additional curves; one associated with the dummy variable (@month=6) being equal to 0 (false) and the other associated with the dummy variable being equal to 1 (true).
Cross-references
See
“Impulse Response Analysis” for a discussion of impulse responses in VARs.