324—Appendix B. Command Reference
lambda=arg Set smoothing parameter value to arg; a larger number results in greater smoothing.
power=arg Set smoothing parameter value using the frequency (default=2) power rule of Ravn and Uhlig (2002) (the number of
periods per year divided by 4, raised to the power arg, and multiplied by 1600).
Hodrick and Prescott recommend the value 2; Ravn and
Uhlig recommend the value 4.
If no smoothing option is specified, EViews will use the power rule with a value of 2.
Other Options
pPrint the graph of the smoothed series and the original series.
Examples
gdp.hpf(lambda=1000) gdp_hp
smooths the GDP series with a smoothing parameter “1000” and saves the smoothed series as GDP_HP.
Cross-references
See “Hodrick-Prescott Filter” on page 354 of the User’s Guide for details.
impulse |
Var View |
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Display impulse response functions of var object with an estimated VAR or VEC.
Syntax
Var View: |
var_name.impulse(n, options) ser1 [ser2 ser3 ...] [@ shock_series |
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[@ ordering_series]] |
You must specify the number of periods n for which you wish to compute the impulse responses.
List the series names in the var whose responses you would like to compute . You may optionally specify the sources of shocks. To specify the shocks, list the series after an “@”. By default, EViews computes the responses to all possible sources of shocks using the ordering in the Var.
impulse—325
If you are using impulses from the Cholesky factor, you may change the Cholesky ordering by listing the order of the series after a second “@”.
Options
g (default) |
Display combined graphs, with impulse responses of |
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one variable to all shocks shown in one graph. If you |
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choose this option, standard error bands will not be dis- |
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played. |
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m |
Display multiple graphs, with impulse response to each |
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shock shown in separate graphs. |
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t |
Tabulate the impulse responses. |
a |
Accumulate the impulse responses. |
imp=arg |
Type of factorization for the decomposition: unit |
(default=“chol”) |
impulses, ignoring correlations among the residuals |
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(“imp=unit”), non-orthogonal, ignoring correlations |
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among the residuals (“imp=nonort”), Cholesky with |
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d.f. correction (“imp=chol”), Cholesky without d.f. |
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correction (“imp=mlechol”), Generalized |
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(“imp=gen”), structural (“imp=struct”), or user speci- |
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fied (“imp=user”). |
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The structural factorization is based on the estimated |
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structural VAR. To use this option, you must first esti- |
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mate the structural decomposition; see svar (p. 494). |
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For user-specified impulses, you must specify the name |
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of the vector/matrix containing the impulses using the |
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“fname=” option. |
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The option “imp=mlechol” is provided for backward |
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compatibility with EViews 3.x and earlier. |
fname=name |
Specify name of vector/matrix containing the impulses. |
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The vector/matrix must have k rows and 1 or k col- |
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umns, where k is the number of endogenous variables. |
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326—Appendix B. Command Reference
se=arg |
Standard error calculations: “se=a” (analytic), |
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“se=mc” (Monte Carlo). |
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If selecting Monte Carlo, you must specify the number |
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of replications with the “rep=” option. |
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Note the following: |
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(1) Analytic standard errors are currently not available |
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for (a) VECs and (b) structural decompositions identi- |
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fied by long-run restrictions. The “se=a” option will be |
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ignored for these cases. |
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(2) Monte Carlo standard errors are currently not avail- |
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able for (a) VECs and (b) structural decompositions. |
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The “se=mc” option will be ignored for these cases. |
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rep=integer |
Number of Monte Carlo replications to be used in com- |
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puting the standard errors. Must be used with the |
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“se=mc” option. |
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matbys=name |
Save responses by shocks (impulses) in named matrix. |
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The first column is the response of the first variable to |
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the first shock, the second column is the response of |
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the second variable to the first shock, and so on. |
matbyr=name |
Save responses by response series in named matrix. |
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The first column is the response of the first variable to |
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the first shock, the second column is the response of |
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the first variable to the second shock, and so on. |
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p |
Print the results. |
Examples
var var1.ls 1 4 m1 gdp cpi
var1.impulse(10,m) gdp @ m1 gdp cpi
The first line declares and estimates a VAR with three variables. The second line displays multiple graphs of the impulse responses of GDP to shocks to the three series in the VAR using the ordering as specified in VAR1.
var1.impulse(10,m) gdp @ m1 @ cpi gdp m1
displays the impulse response of GDP to a one standard deviation shock in M1 using a different ordering.
jbera—327
Cross-references
See Chapter 24, “Vector Autoregression and Error Correction Models”, on page 719 of the User’s Guide for a discussion of variance decompositions in VARs.
See also decomp (p. 270).
jbera |
Var View |
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Multivariate residual normality test. |
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Syntax |
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Var View: |
var_name.jbera(options) |
You must specify a factorization method using the “factor=” option.
Options
factor=chol |
Factorization by the inverse of the Cholesky factor of |
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the residual covariance matrix. |
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factor=cor |
Factorization by the inverse square root of the residual |
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correlation matrix (Doornik and Hansen, 1994). |
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factor=cov |
Factorization by the inverse square root of the residual |
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covariance matrix (Urzua, 1997). |
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factor=svar |
Factorization matrix from structural VAR. You must first |
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estimate the structural factorization to use this option; |
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see svar (p. 494). |
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name=arg |
Save the test statistics in a named matrix object. See |
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below for a description of the statistics contained in the |
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stored matrix. |
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p |
Print the test results. |
The “name=” option stores the following matrix. Let the VAR have k endogenous variables. Then the stored matrix will have dimension ( k + 1 ) × 4 . The first k rows contain statistics for each orthogonal component, where the first column contains the third moments, the second column contains the χ21 statistics for the third moments, the third column contains the fourth moments, and the fourth column holds the χ21 statistics for the fourth moments. The sum of the second and fourth columns are the Jarque-Bera statistics reported in the last output table.