308—Appendix B. Command Reference
garch |
Equation View |
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Conditional standard deviation graph of (G)ARCH equation.
Displays the conditional standard deviation or conditional variance graph of an equation estimated by ARCH.
Syntax
Equation View: eq_name.garch(options)
Options
vDisplay conditional variance graph instead of the standard deviation graph.
p |
Print the graph |
Examples
equation eq1.arch sp500 c
eq1.garch
estimates a GARCH(1,1) model and displays the estimated conditional standard deviation graph.
eq1.garch(v, p)
displays and prints the estimated conditional variance graph.
Cross-references
ARCH estimation is described in Chapter 20, “ARCH and GARCH Estimation”, on page 599 of the User’s Guide.
See also arch (p. 206) and makegarch (p. 352).
genr
Command || Object Declaration (Alpha) | Object Declaration (Series) | Pool Proc
Generate series.
Generate series or alphas. This procedure also allows you to generate multiple series using the cross-section identifiers in a pool.
genr—309
Syntax
Command: genr ser_name = expression
Pool Proc: |
pool_name.genr ser_name = expression |
For pool series generation, you may use the cross section identifier “?” in the series name and/or in the expression on the right-hand side.
Examples
genr y = 3 + x
generates a numeric series that takes the values from the series X and adds 3.
genr full_name = first_name + last_name
creates an alpha series formed by concatenating the alpha series FIRST_NAME and LAST_NAME.
The commands,
pool pool1
pool1.add 1 2 3
pool1.genr y? = x? - @mean(x?)
are equivalent to generating separate series for each cross-section:
genr y1 = x1 - @mean(x1) genr y2 = x2 - @mean(x2) genr y3 = x3 - @mean(x3)
Similarly:
pool pool2 pool2.add us uk can
pool2.genr y_? = log(x_?) - log(x_us)
generates three series Y_US, Y_UK, Y_CAN that are the log differences from X_US. Note that Y_US=0.
It is worth noting that the pool genr command simply loops across the cross-section identifiers, performing the evaluations using the appropriate substitution. Thus, the command,
pool2.genr z = y_?
is equivalent to entering:
genr z = y_us
genr z = y_uk
310—Appendix B. Command Reference
genr z = y_can
so that upon completion, the ordinary series Z will contain Y_CAN.
Cross-references
See Chapter 27, “Pooled Time Series, Cross-Section Data”, on page 823 of the User’s Guide for a discussion of the computation of pools, and a description of individual and balanced samples.
See series (p. 442) and alpha (p. 204) for a discussion of the expressions allowed in genr.
gmm |
Command || Equation Method | System Method |
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Estimation by generalized method of moments (GMM).
The equation or system object must be specified with a list of instruments.
Syntax
Command: gmm(options) y x1 [x2 x3 ...] @ z1 [z2 z3 ...]
gmm(options) specification @ z1 [z2 z3 ...]
Equation Method: eq_name.gmm(options) y x1 [x2 x3 ...] @ z1 [z2 z3 ...]
eq_name.gmm(options) specification @ z1 [z2 z3 ...]
System Method: system_name.gmm(options)
To use gmm as a command or equation method, follow the name of the dependent variable by a list of regressors, followed by the “@” symbol, and a list of instrumental variables which are orthogonal to the residuals. Alternatively, you can specify an expression using coefficients, an “@” symbol, and a list of instrumental variables. There must be at least as many instrumental variables as there are coefficients to be estimated.
In panel settings, you may specify dynamic instruments corresponding to predetermined variables. To specify a dynamic instrument, you should tag the instrument using “@DYN”, as in “@DYN(X)”. By default, EViews will use a set of period-specific instruments corresponding to lags from -2 to “-infinity”. You may also specify a restricted lag range using arguments in the “@DYN” tag. For example, to use lags from-5 to “-infinity” you may enter “@DYN(X, -5)”; to specify lags from -2 to -6, use “@DYN(X, -2, -6)” or “@DYN(X, -6, -2)”.
Note that dynamic instrument specifications may easily generate excessively large numbers of instruments.
gmm—311
Options
General Options
m=integer |
Maximum number of iterations. |
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c=number |
Set convergence criterion. The criterion is based upon |
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the maximum of the percentage changes in the scaled |
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coefficients. |
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l=number |
Set maximum number of iterations on the first-stage |
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iteration to get the one-step weighting matrix. |
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showopts / |
[Do / do not] display the starting coefficient values and |
-showopts |
estimation options in the estimation output. |
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deriv=keyword |
Set derivative methods. The argument keyword should |
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be a oneor two-letter string. The first letter should |
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either be “f” or “a” corresponding to fast or accurate |
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numeric derivatives (if used). The second letter should |
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be either “n” (always use numeric) or “a” (use analytic |
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if possible). If omitted, EViews will use the global |
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defaults. |
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p |
Print results. |
Additional Options for Non-Panel Equation and System estimation
w |
Use White’s diagonal weighting matrix (for cross sec- |
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tion data). |
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b=arg |
Specify the bandwidth: “nw” (Newey-West fixed band- |
(default=“nw”) |
width based on the number of observations), “number” |
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(user specified bandwidth), “v” (Newey-West auto- |
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matic variable bandwidth selection), “a” (Andrews |
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automatic selection). |
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q |
Use the quadratic kernel. Default is to use the Bartlett |
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kernel. |
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n |
Prewhiten by a first order VAR before estimation. |
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i |
Iterate simultaneously over the weighting matrix and |
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the coefficient vector. |
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s |
Iterate sequentially over the weighting matrix and coef- |
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ficient vector. |
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o (default) |
Iterate only on the coefficient vector with one step of |
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the weighting matrix. |
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312—Appendix B. Command Reference
c |
One step (iteration) of the coefficient vector following |
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one step of the weighting matrix. |
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e |
TSLS estimates with GMM standard errors. |
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Additional Options for Panel Equation estimation
cx=arg |
Cross-section effects method: (default) none, fixed |
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effects estimation (“cx=f”), first-difference estimation |
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(“cx=fd”), orthogonal deviation estimation (“cx=od”) |
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per=arg |
Period effects method: (default) none, fixed effects esti- |
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mation (“per=f”). |
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levelper |
Period dummies always specified in levels (even if one |
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of the transformation methods is used, “cx=fd” or |
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“cx=od”). |
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wgt=arg |
GLS weighting: (default) none, cross-section system |
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weights (“wgt=cxsur”), period system weights |
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(“wgt=persur”), cross-section diagonal weighs |
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(“wgt=cxdiag”), period diagonal weights (“wgt=per- |
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diag”). |
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gmm=arg |
GMM weighting: 2SLS (“gmm=2sls”), White period |
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system covariances (Arellano-Bond 2-step/n-step) |
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(“gmm=perwhite”), White cross-section system |
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(“gmm=cxwhite”), White diagonal |
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(“gmm=stackedwhite”), Period system (“gmm=per- |
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sur”), Cross-section system (“gmm=cxsur”), Period |
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heteroskedastic (“cov=perdiag”), Cross-section het- |
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eroskedastic (“gmm=cxdiag”). |
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By default, uses the identity matrix unless estimated |
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with first difference transformation (“cx=fd”), in |
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which case, uses (Arellano-Bond 1-step) difference |
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weighting matrix. In this latter case, you should specify |
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2SLS weights (“gmm=2sls”) for Anderson-Hsiao esti- |
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mation. |
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gmm—313
cov=arg |
Coefficient covariance method: (default) ordinary, |
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White cross-section system robust (“cov=cxwhite”), |
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White period system robust (“cov=perwhite”), White |
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heteroskedasticity robust (“cov=stackedwhite”), Cross- |
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section system robust/PCSE (“cov=cxsur”), Period sys- |
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tem robust/PCSE (“cov=persur”), Cross-section het- |
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eroskedasticity robust/PCSE (“cov=cxdiag”), Period |
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heteroskedasticity robust (“cov=perdiag”). |
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keepwgts |
Keep full set of GLS/GMM weights used in estimation |
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with object, if applicable (by default, only weights |
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which take up little memory are saved). |
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nodf |
Do not perform degree of freedom corrections in com- |
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puting coefficient covariance matrix. The default is to |
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use degree of freedom corrections. |
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coef=arg |
Specify the name of the coefficient vector (if specified |
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by list); the default behavior is to use the “C” coeffi- |
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cient vector. |
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iter=arg |
Iteration control for GLS and GMM weighting specifica- |
(default=“onec”) |
tions: perform one weight iteration, then iterate coeffi- |
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cients to convergence (“iter=onec”), iterate weights |
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and coefficients simultaneously to convergence |
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(“iter=sim”), iterate weights and coefficients sequen- |
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tially to convergence (“iter=seq”), perform one weight |
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iteration, then one coefficient step (“iter=oneb”). |
Note that some options are only available for a subset of specifications.
Examples
In a non-panel workfile, we may estimate equations using the standard GMM options. The specification:
eq.gmm(w) y c f k @ c z1 z2 z3
estimates the linear specification using a White diagonal weighting matrix (one-step, with no iteration). The command:
eq.gmm(e, b=v) c(1) + c(2)*(m^c(1) + k^(1-c(1))) @ c z1 z2 z3
estimates the nonlinear model using two-stage least squares (instrumental variables) with GMM standard errors computed using Newey-West automatic bandwidth selected weights.
For system estimation, the command:
314—Appendix B. Command Reference
sys1.gmm(b=a, q, i)
estimates the system SYS1 by GMM with a quadratic kernel, Andrews automatic bandwidth selection, and iterates until convergence.
When working with a workfile that has a panel structure, you may use the panel equation estimation options. The command
eq.gmm(cx=fd, per=f) dj dj(-1) @ @dyn(dj)
estimates an Arellano-Bond “1-step” estimator with differencing of the dependent variable DJ, period fixed effects, and dynamic instruments constructed using DJ with observation specific lags from period t − 2 to 1.
To perform the “2-step” version of this estimator, you may use:
eq.gmm(cx=fd, per=f, gmm=perwhite, iter=oneb) dj dj(-1) @ @dyn(dj)
where the combination of the options “gmm=perwhite” and (the default) “iter=oneb” instructs EViews to estimate the model with the difference weights, to use the estimates to form period covariance GMM weights, and then re-estimate the model.
You may iterate the GMM weights to convergence using:
eq.gmm(cx=fd, per=f, gmm=perwhite, iter=seq) dj dj(-1) @ @dyn(dj)
Alternately:
eq.gmm(cx=od, gmm=perwhite, iter=oneb) dj dj(-1) x y @ @dyn(dj,-2,-6) x(-1) y(-1)
estimates an Arellano-Bond “2-step” equation using orthogonal deviations of the dependent variable, dynamic instruments constructed from DJ from period t − 6 to t − 2 , and ordinary instruments X(-1) and Y(-1).
Cross-references
See Chapter 16, “Additional Regression Methods”, on page 459, Chapter 23, “System Estimation”, on page 693, and Chapter 29, “Panel Estimation”, on page 899 of the User’s Guide for discussion of the various GMM estimation techniques.