344—Appendix B. Command Reference
gdp.linkto(c=s) quarterly\gdp
links to GDP in the QUARTERLY page, and will frequency convert by summing the nonmissing observations.
Cross-references
For a detailed discussion of linking, see Chapter 8, “Series Links”, on page 175 of the
User’s Guide.
See also link (p. 338), unlink (p. 519), and copy (p. 249).
load |
Command |
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Load a workfile.
Provided for backward compatibility. Same as wfopen (p. 532).
.
logit |
Command |
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Estimate binary models with logistic errors.
Provide for backward compatibility. Equivalent to issuing the command, binary with the option “(d=l)”.
See binary (p. 222).
logl |
Object Declaration |
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Declare likelihood object. |
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Syntax |
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Command: |
logl logl_name |
Examples
logl ll1
declares a likelihood object named LL1.
ll1.append @logl logl1
ll1.append res1 = y-c(1)-c(2)*x
ll1.append logl1 = log(@dnorm(res1/@sqrt(c(3))))-log(c(3))/2
ls—345
specifies the likelihood function for LL1 and estimates the parameters by maximum likelihood.
Cross-references
See Chapter 22, “The Log Likelihood (LogL) Object”, on page 669 of the User’s Guide for further examples of the use of the likelihood object.
See also append (p. 205) for adding specification lines to an existing likelihood object, and ml (p. 369) for estimation.
ls |
Command || Equation Method | Pool Method | System Method | Var |
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Method |
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Estimation by linear or nonlinear least squares regression.
When used as a pool proc or when the current workfile has a panel structure, ls also estimates cross-section weighed least squares, feasible GLS, and fixed and random effects models.
Syntax
Command: ls(options) y x1 [x2 x3 ...]
ls(options) specification
Equation Method: eq_name.ls(options) y x1 [x2 x3 ...]
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eq_name.ls(options) specification |
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paneleq_name.ls(options) y x1 [x2 x3 ...] [@cxreg z1 z2 ...] |
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[@perreg z3 z4 ...] |
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paneleq_name.ls(options) specification |
Pool Method: |
pool_name.ls(options) y x1 [x2 x3 ...] [@cxreg z1 z2 ...] [@perreg |
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z3 z4 ...] |
System Method: |
system_name.ls(options) |
VAR Method: |
var_name.ls(options) lag_pairs endog_list [@ exog_list] |
Additional issues associated with each usage of the keyword are described in the following sections.
Equations
For linear specifications, list the dependent variable first, followed by a list of the independent variables. Use a “C” if you wish to include a constant or intercept term; unlike some programs, EViews does not automatically include a constant in the regression. You may add AR, MA, SAR, and SMA error specifications, and PDL specifications for polynomial
346—Appendix B. Command Reference
distributed lags. If you include lagged variables, EViews will adjust the sample automatically, if necessary.
Both dependent and independent variables may be created from existing series using standard EViews functions and transformations. EViews treats the equation as linear in each of the variables and assigns coefficients C(1), C(2), and so forth to each variable in the list.
Linear or nonlinear single equations may also be specified by explicit equation. You should specify the equation as a formula. The parameters to be estimated should be included explicitly: “C(1)”, “C(2)”, and so forth (assuming that you wish to use the default coefficient vector “C”). You may also declare an alternative coefficient vector using coef and use these coefficients in your expressions.
Pools
When used as a pool method, ls carries out pooled data estimation. Type the name of the dependent variable followed by one or more lists of regressors. The first list should contain ordinary and pool series that are restricted to have the same coefficient across all members of the pool. The second list, if provided, should contain pool variables that have different coefficients for each cross-section member of the pool. If there is a cross-section specific regressor list, the two lists must be separated by “@CXREG”. The third list, if provided, should contain pool variables that have different coefficients for each period. The list should be separated from the previous lists by “@PERREG”.
For pool estimation, you may include AR terms as regressors in either the common or cross-section specific lists. AR terms are, however, not allowed for some estimation methods. MA terms are not supported.
VARs
ls estimates an unrestricted VAR using equation-by-equation OLS. You must specify the order of the VAR (using one or more pairs of lag intervals), and then provide a list of series or groups to be used as endogenous variables. You may include exogenous variables such as trends and seasonal dummies in the VAR by including an “@-sign” followed by a list of series or groups. A constant is automatically added to the list of exogenous variables; to estimate a specification without a constant, you should use the option “noconst”.
Options
General options
m=integer |
Set maximum number of iterations. |
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c=scalar |
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. |
ls—347
sUse the current coefficient values in “C” as starting values for equations with AR or MA terms (see also param (p. 404)).
s=number |
Determine starting values for equations specified by list |
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with AR or MA terms. Specify a number between zero |
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and one representing the fraction of preliminary least |
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squares estimates computed without AR or MA terms to |
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be used. Note that out of range values are set to “s=1”. |
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Specifying “s=0” initializes coefficients to zero. By |
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default EViews uses “s=1”. |
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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 one or 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 basic estimation results. |
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Additional Options for Equation, System, and Var estimation
w = |
Weighted Least Squares. Each observation will be |
series_name |
weighted by multiplying by the specified series. |
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h |
White’s heteroskedasticity consistent standard errors. |
nNewey-West heteroskedasticity and autocorrelation consistent (HAC) standard errors.
z |
Turn off backcasting in ARMA models. |
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noconst |
Do not include a constant in exogenous regressors list |
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for VARs. |
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Note: not all options are available for all equation methods. See the User’s Guide for details on each estimation method.
Additional Options for Pool and Panel Equation estimation
cx=arg |
Cross-section effects: (default) none, fixed effects |
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(“cx=f”), random effects (“cx=r”). |
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348—Appendix B. Command Reference
per=arg |
Period effects: (default) none, fixed effects (“per=f”), |
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random effects (“per=r”). |
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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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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/PCSE (“cov=perdiag”). |
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keepwgts |
Keep full set of GLS weights used in estimation with |
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object, if applicable (by default, only small memory |
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weights are saved). |
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rancalc=arg |
Random component method: Swamy-Arora (“ran- |
(default=“sa”) |
calc=sa”), Wansbeek-Kapteyn (“rancalc=wk”), Wal- |
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lace-Hussain (“rancalc=wh”). |
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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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bEstimate using a balanced sample (pool estimation only).
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- |
|
cient vector. |
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iter=arg |
Iteration control for GLS specifications: perform one |
(default= |
weight iteration, then iterate coefficients to convergence |
“onec”) |
(“iter=onec”), iterate weights and coefficients simulta- |
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neously to convergence (“iter=sim”), iterate weights |
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and coefficients sequentially to convergence |
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(“iter=seq”), perform one weight iteration, then one |
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coefficient step (“iter=oneb”). |
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Note that random effects models currently do not per- |
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mit weight iteration to convergence. |
ls—349
Examples
equation eq1.ls m1 c uemp inf(0 to -4) @trend(1960:1)
estimates a linear regression of M1 on a constant, UEMP, INF (from current up to four lags), and a linear trend.
equation eq2.ls(z) d(tbill) c inf @seas(1) @seas(1)*inf ma(2)
regresses the first difference of TBILL on a constant, INF, a seasonal dummy, and an interaction of the dummy and INF, with an MA(2) error. The “z” option turns off backcasting.
coef(2) beta
param beta(1) .2 beta(2) .5 c(1) 0.1
equation eq3.ls(h) q = beta(1)+beta(2)*(l^c(1) + k^(1-c(1)))
estimates the nonlinear regression starting from the specified initial values. The “h” option reports heteroskedasticity consistent standard errors.
equation eq4.ls r = c(1)+c(2)*r(-1)+div(-1)^c(3)
sym betacov = eq4.@cov
declares and estimates a nonlinear equation and stores the coefficient covariance matrix in a symmetric matrix called BETACOV.
pool1.ls dy? inv? edu? year
estimates pooled OLS of DY? on a constant, INV?, EDU? and YEAR.
pool1.ls(cx=f) dy? @cxreg inv? edu? year ar(1)
estimates a fixed effects model without restricting any of the coefficients to be the same across pool members.
group rhs c dum1 dum2 dum3 dum4
ls cons rhs ar(1)
uses the group definition for RHS to regress CONS on C, DUM1, DUM2, DUM3, and DUM4, with an AR(1) correction.
Cross-references
Chapter 15, “Basic Regression”, on page 441 and Chapter 16, “Additional Regression Methods”, on page 459 of the User’s Guide discuss the various regression methods in greater depth.
See Chapter 27, “Pooled Time Series, Cross-Section Data”, on page 823 of the User’s Guide for a discussion of pool estimation, and Chapter 29, “Panel Estimation”, on page 899 of the User’s Guide for a discussion of panel equation estimation.