, or to place inequality restrictions on the coefficient values, you may do so using special expressions of the form:
.penalty=arg (default=“el”) | Type of threshold estimation: “enet” (elastic net), “ridge” (ridge), “lasso” (Lasso). |
alpha=arg (default=“.5”) | Value of the mixing parameter. Must be a value from zero to one. |
lambda=arg | Value(s) of the penalty parameter. Can be one or more numbers or vector objects.Values must be zero or greater. If left blank (default) EViews will generate a list. |
ytrans=arg (default=“none”) | Scaling of the dependent variable: “none” (none), “L1” (L1), “L2” (L2), “stdsmpl” (sample standard deviation), “stdpop” (population standard deviation), “minmax” (min-max). |
xtrans=arg (default=“stdpop”) | Scaling of the regressor variables: “none” (none), “L1” (L1 norm), “L2” (L2 norm), “stdsmpl” (sample standard deviation), “stdpop” (population standard deviation), “minmax” (min-max). |
nlambdas=integer (default=100) | Number of penalty values for EViews-supplied list. |
lambdaratio=arg | Ratio of minimum to maximum lambda for EViews-supplied list. You may specify a value for the ratio parameter, or you may leave the edit field blank to let EViews specify a default value based on the number of observations  and the number of potential regressors  .By default, EViews will set the ratio to 0.001. |
lambdawgt= vector_name | Vector of individual penalty weights, containing non-negative values sized to and matching the order of the variables in the specification. If a vector is provided and individual weights are specified using one or more @vw regressors, the vector weights will be applied first, then overwritten by the individual variable weights. For comparability purposes, we normalize the final weights so that they sum to  where  the number of non-zero  . |
nlambdamin=integer (default=5) | Minimum number of lambda values in the path before applying stopping rules. |
minddev=arg (default=1e-05) | Minimum change in deviance fraction to continue estimation. Truncate path estimation if relative change in deviance is smaller than this value. |
maxedev=arg (default=0.99) | Maximum of deviance explained fraction attained to terminate estimation. Truncate path estimation if fraction of null deviance explained is larger than this value. |
maxvars=arg | Maximum number of regressors in the model. Truncate path estimation if the number of coefficients (including those for non-penalized variables like the intercept) reaches this value. |
maxvarsratio=arg | Maximum number of regressors in the model as a fraction of the number of observations. Truncate path estimation if the number of coefficients (including those for non-penalized variables like the intercept) divided by the number of observations reaches this value. |
cvmethod=arg (default=“kfold_cv”) | Cross-validation method: “kfold” (k-fold), “simple” (simple split), “mcarlo” (Monte Carlo), “leavepout” (leave-P-out), “leave1out” (leave-1-out), “rolling” (rolling window), “expanding” (expanding window). |
cvmeasure=arg (default=“mse”) | Cross-validation fit measure: “mse” (mean-squared error), “r2” (R‑squared), “mae” (mean absolute error), “mape” (mean absolute percentage error), “smape” (symmetric mean absolute percentage error). |
cvnfolds=arg (default=5) | Number of folds for K-fold cross-validation. For “cvmethod=kfold”. |
cvftrain=arg (default=0.8) | Proportion of data for split and Monte Carlo methods. For “cvmethod=simple” and “cvmethod=mcarlo”. |
cvnreps=arg (default=1) | Number of Monte Carlo method repetitions. For “cvmethod=mcarlo”. |
cvleaveout=arg (default=2) | Number of data points left out for leave-p-out method. For “cvmethod=leavepout”. |
cvnwindows=arg (default=4) | Number of windows for rolling window cross-validation method. For “cvmethod=rolling”. |
cvinitial=arg (default=12) | Number of initial data points in the training set for expanding cross-validation. For “cvmethod=expanding”. |
cvpregap=arg (default=0) | Number of observations between end of training set and beginning of test set. For “cvmethod=simple”, “cvmethod=rolling” and “cvmethod=expanding”. |
cvhorizon=arg (default=1) | Number of observation in the test set. For “cvmethod=rolling” and “cvmethod=expanding”. |
cvpostgap=arg (default=0) | Number of observations between end of test set and beginning of next training set for rolling window or between end of test set and end of next training set for expanding window. For “cvmethod=rolling” and “cvmethod=expanding” |
seed=positive_integer from 0 to 2,147,483,647 | Seed the random number generator. If not specified, EViews will seed random number generator with a single integer draw from the default global random number generator. |
Type of random number generator: improved Knuth generator (“kn”), improved Mersenne Twister (“mt”), Knuth’s (1997) lagged Fibonacci generator used in EViews 4 (“kn4”) L’Ecuyer’s (1999) combined multiple recursive generator (“le”), Matsumoto and Nishimura’s (1998) Mersenne Twister used in EViews 4 (“mt4”). |
coefmin= vector_name, number | Vector of individual coefficient minimum values, containing negative or missing values sized to and matching the order of the variables in the specification, or a negative value for the minimum for all coefficients. Missing values in the vector should be used to indicate that the coefficient is unrestricted. If a vector of values is provided and individual minimums are specified using one or more @vw regressors, the vector values will be applied first, then overwritten by the individual values. |
coefmax= vector_name, number | Vector of individual coefficient maximum values, containing positive or missing values sized to and matching the order of the variables in the specification, or a positive value for the maximum for all coefficients. Missing values in the vector should be used to indicate that the coefficient is unrestricted. If a vector of values is provided and individual maximums are specified using one or more @vw regressors, the vector values will be applied first, then overwritten by the individual values. |
maxit=integer | Maximum number of iterations. |
conv=scalar | Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled estimates. The criterion will be set to the nearest value between 1e-24 and 0.2. |
w=arg | Weight series or expression. |
wtype=arg (default=“istdev”) | Weight specification type: inverse standard deviation (“istdev”), inverse variance (“ivar”), standard deviation (“stdev”), variance (“var”). |
wscale=arg | Weight scaling: EViews default (“eviews”), average (“avg”), none (“none”). The default setting depends upon the weight type: “eviews” if “wtype=istdev”, “avg” for all others. |
showopts / ‑showopts | [Do / do not] display estimation options in the output. |
prompt | Force the dialog to appear from within a program. |
p | Print basic results view after estimation. |
equal to the default of 0.9, no regressor or dependent variable scaling, automatically determined 100-element lambda path with minimum lambda of 0.0001 times the maximum value, using the default K-fold cross-validation with 5 folds with an MSE objective and a random generator seed of 513255899 to determine the optimal value.
equal to the 0.75 using rolling window cross-validation and SMAPE cross-validation.