method=arg (default = “ls”)

Set the method of estimation: "ls" (least-squares regression, default) or "qreg" (quantile regression).

determ=arg (default = “rconst”)

Johansen deterministic trend type: “none” (no deterministics), “rconst” (restricted constant and no trend), “uconst” (unrestricted constant and no trend), “rtrend” (unrestricted constant and restricted trend, “utrend” (unrestricted constant and unrestricted trend).

trend=arg (deprecated)

Johansen deterministic trend type: “none” (no deterministics), “const” (restricted constant and no trend, default), “uconst” (unrestricted constant and no trend), “linear” (unrestricted constant and restricted trend, “ulinear” (unrestricted constant and unrestricted trend).

Note: this is a deprecated s option which handles a subset of cases covered by the “determ=” option

fixed

Do not use automatic selection for lag lengths. This option must be used with the “deplags=” and “reglags=” options.

deplags=int (default = 4)

Set the number of lags for the dependent variable to int. If automatic selection is used, this sets the maximum number of possible lags. If fixed lags are used (the fixed option is set), this fixes the number of lags.

reglags=int (default = 4)

Set the number of lags for the explanatory variables (dynamic regressors) to int. If automatic selection is used, this sets the maximum number of possible lags. If fixed lags are used (the fixed option is set), this fixes the number of lags for each regressor.

ic=key (default =“aic”)

Set the method of automatic model selection. key may take values of “aic” (Akaike information criterion, default), “bic” (Schwarz criterion), “hq” (Hannan-Quinn criterion) or “rbar2” (Adjusted R-squared, not applicable in panel workfiles).

cov=arg

Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich method), “hac” (Newey-West HAC, available for nonlinear least squares or ARMA estimated by CLS)..

nodf

Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections.

covlag=arg (default=1)

Whitening lag specification: integer (user-specified lag value), “a” (automatic selection).

covinfosel=arg (default=“aic”)

Information criterion for automatic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lag=a”).

covmaxlag=integer

covkern=arg (default=“bart”)

Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniel), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen-Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen).

covbw=arg (default=“fixednw”)

Kernel Bandwidth: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey-West automatic), number (User-specified bandwidth).

covnwlag=integer

Newey-West lag-selection parameter for use in nonparametric kernel bandwidth selection (if “covbw=neweywest”).

covbwint

Use integer portion of bandwidth.

coef=arg

Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vector.

prompt

Force the dialog to appear from within a program.

p

Print results.

quant=number (default = 0.5)

Quantile to be fit (where number is a value between 0 and 1).

w=arg

Weight series or expression.

Note: we recommend that, absent a good reason, you employ the default settings Inverse std. dev. weights (“wtype=istdev”) with EViews default scaling (“wscale=eviews”) for backward compatibility with versions prior to EViews 7.

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.

cov=arg (default=“sandwich”)

Method for computing coefficient covariance matrix: “iid” (ordinary estimates), “sandwich” (Huber sandwich estimates), “boot” (bootstrap estimates).

When “cov=iid” or “cov=sandwich”, EViews will use the sparsity nuisance parameter calculation specified in “spmethod=” when estimating the coefficient covariance matrix.

bwmethod=arg (default = “hs”)

Method for automatically selecting bandwidth value for use in estimation of sparsity and coefficient covariance matrix: “hs” (Hall-Sheather), “bf” (Bofinger), “c” (Chamberlain).

bw =number

Use user-specified bandwidth value in place of automatic method specified in “bwmethod=”.

bwsize=number (default = 0.05)

Size parameter for use in computation of bandwidth (used when “bw=hs” and “bw=bf”).

spmethod=arg (default=“kernel”)

Sparsity estimation method: “resid” (Siddiqui using residuals), “fitted” (Siddiqui using fitted quantiles at mean values of regressors), “kernel” (Kernel density using residuals)

Note: “spmethod=resid” is not available when “cov=sandwich”.

btmethod=arg (default= “pair”)

Bootstrap method: “resid” (residual bootstrap), “pair” (xy-pair bootstrap), “mcmb” (MCMB bootstrap), “mcmba” (MCMB-A bootstrap).

btreps=integer (default=100)

Number of bootstrap repetitions

btseed=positive integer

Seed the bootstrap random number generator.

If not specified, EViews will seed the bootstrap random number generator with a single integer draw from the default global random number generator.

btrnd=arg (default=“kn” or method previously set using rndseed).

Type of random number generator for the bootstrap: 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”).

btobs=integer

Number of observations for bootstrap subsampling (when “bsmethod=pair”).

Should be significantly greater than the number of regressors and less than or equal to the number of observations used in estimation. EViews will automatically restrict values to the range from the number of regressors and the number of estimation observations.

If omitted, the bootstrap will use the number of observations used in estimation.

btout=name

(optional) Matrix to hold results of bootstrap simulations.

k=arg (default=“e”)

Kernel function for sparsity and coefficient covariance matrix estimation (when “spmethod=kernel”): “e” (Epanechnikov), “r” (Triangular), “u” (Uniform), “n” (Normal–Gaussian), “b” (Biweight–Quartic), “t” (Triweight), “c” (Cosinus).

m=integer

Maximum number of iterations.

s

Use the current coefficient values in estimator coefficient vector as starting values (see also param).

s=number (default =0)

Determine starting values for equations. Specify a number between 0 and 1 representing the fraction of preliminary least squares coefficient estimates.

Note that out of range values are set to the default.

coef=arg

Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vector.

showopts / ‑showopts

[Do / do not] display the starting coefficient values and estimation options in the estimation output.

prompt

Force the dialog to appear from within a program.

p

Print estimation results.