The no-autocorrelation null hypothesis is also not rejected by the Durbin-Watson test test statistic is given in the regression output is equal to 1.959 which is in the acceptance region.
#FACTOR ANALYSIS EVIEWS SERIES#
Their p-values both confirm the absence of series autocorrelation up to the second order. In the upper panel of the Breusch-Godfrey test output there are two versions of the test statistic which are asymptotically equivalent.
#FACTOR ANALYSIS EVIEWS SERIAL#
Another way to test for series correlation is to perform Breusch-Godfrey Test - in EViews this can be done through Serial Correlation LM Test. Low p-values indicate absence of serial autocorrelations up to lag 10. The correlogram of the residuals from the factor model is given in Figure? Correlogram - Q-statistic provides values of the Box-Ljung statistics to test the significance of autocorrelations of residuals. This can be done in the section View/Residual Tests. Residuals Diagnostic Before drawing any conclusions from the estimated regression, it is necessary to perform residual diagnostic to make sure that the assumptions of the classic linear regression model are satisfied. Now, the residuals from the CAPM regression for IBM stock returns are stored in the new series object resid_ibm.īesides the standard errors of the coefficient estimators, given in the output window, one can retrieve the whole variance-covariance matrix by clicking on View/Covariance Matrix. This can done by copying the residual series into a new object
Thus, residual series has to be saved for further use, if necessary. Note, that resid contains residuals of the last estimated model and will be lost once the model is reestimated. Residual series is automatically stored in the series object resid which created by EViews in each workfile. View/Actual, Fitted, Residuals creates various plots of the estimated residual series, as well as fitted values of the dependent variable. View/Representation view contains the equation specification of the model, View/Estimation Output provides the familiar model output. As each object in EViews, Equation can be represented in different views. The proportion of market specific risk is R2 = 0.37 and the proportion of firm specific risk is 1 - R2 = 0.63.īy estimating the regression model, EViews produces an object Equation, which can be saved and used later on (press Name button in the top of the equation window). The proportion of the variance Rit explained by the variability in the market index is the usual regression R2 statistic and 1 -R2 is the proportion of the variability of Rn that is due to firm specific factors. Overall significance of the regression is reflected in the value of F-statistic which is high enough to reject the null hypothesis of insignificance of all slope coefficients (p-value is given in Prob (F-statistic).
All the coefficients are highly statistically significant as indicated by low p-values (column Prob). In column t-statistic, the value of the test statistic is provided to test that the hypothesis fii = 0. Slope coefficients denote the sensitivities of the returns on the stock to the three factors and show the impact of systematic factors on returns. Estimation Output The regression estimation output looks as follows The estimated coefficients of the model are given in the column Coefficients (the coefficient in front of C denote estimate of the intercept term).
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