Hence, do I use the wrong regression command or do I have some logical (and statistical) issues/misthinking in calculating t-tests?Ĭode. The intercept represents the mean of 213 individual regressions and thus, it's statistical significance should be assessed on the basis of the standard error and not the standard deviation. More precisely, I regress a portfolio excess return on the Fama-French three factors using WLS methodology and want to assess the statistical significance of the constant/intercept. However, many statistical books and papers provide the one-sample t-tests as the estimate of the coefficient divided by the standard error with the standard error being equal to the standard deviation divided by the square root of the number of observations. After analysing the data I noticed that the standard error of a coefficient corresponds to the square root of the variance of respective coefficient, i.e.
The regression output provides the estimates of the coefficients, the standard error as well as the t-statistic among others. I am performing a regression analysis with multiple factors (see output of the regression analysis below) and I stumbled upon one question.
