Eviews回归分析

adjusted R-squared statistic standard error of the regression

@ssr sum of squared residuals @dw Durbin-Watson statistic @f F-statistic @logl

value of the log-likelihood function

@aic Akaike information criterion @sc @jstat

Schwarz information criterion

scalar containing the J-statistic (for GMM)

@regobs number of observations in regression @meandep mean of the dependent variable

@sddep standard deviation of the dependent variable @ncoef

total number of estimated coefficients

@coefs(i) coefficient i, where i is given by the order in which the

coefficients appear in the representations view

@stderrs(i)

standard error for coefficient i

@tstats(i) t-statistic value for coefficient i @cov(i,j) covariance of coefficients i and j Functions that return vector or matrix objects: @coefs @stderrs

vector of coefficient values

vector of standard errors for the coefficients

@tstats vector of ratios of coefficients to standard errors @cov matrix containing the coefficient covariance matrix For example: series y = eq1.@dw vector tstats = eq1.@tstats matrix mycov = eq1.@cov

scalar pvalue = 1-@cnorm(@abs(eq1.@tstats(4)))

scalar var1 = eq1.@covariance(1,1)