According to the textbook the acceptable zone is 1.5-2, but what about between 2-2.5? I think they didn't mention values above 2 because we won't encounter values about 2 in this course maybe. When DW approaches 0 there is positive autocorrelation, whilst approaching 4, there is negative autocorrelation.
#Eviews output interpretation serial
If there is serial correlation, then we can improve the forecast by forecasting the forecast errors. is there a cut-off value for these?)ĭurbin-Watson stat: Tests for serial correlation in the error term of the regression.
You want both of these to be low and you pick the model with the lower values, but if you just have 1 model are these criteria useless? (i.e. SIC is an alternative to AIC, which penalizes degrees of freedom even more harshly. The Adjusted R-squared is similar but accounts for the number of regressors (so for # of regressors > 1, it will be smaller that R-squaredĪkaike Info Criterion (AIC) and Schwarz criterion (SIC): AIC is used to estimate the out-of-sample forecast error variance, like the Standard Error of the regression, but penalizes degrees of freedom more harshly. You want the R-squared to be as close to 1 as possible, but above 0.5 is alright. R-squared and Adjust r-squared: Measured the in-sample success of the regression equation in forecasting the dependent variable. SE of regression should not be above 10% or 15% of the mean of the dependent variable You want this to be as small as possible because large values means the model didn't fit well to the dependent variable. of Regression: Measures the disturbance of the error term in the regression. You want the EViews output value as much as possible because it means something is significant? (not sure) If none of the variables have predictive value, the F-Statistic follows an F distribution with k-1 and T-k degrees of freedom. You want the value to be as great as possible.į-Statistic: Determines whether or not all the independent variables are jointly irrelevant to the regression (i.e. When disturbances in the regression are normally distributed, maximizing the log-likelihood is the same as minimizing the SSR. Log-Likelihood: The value which maximized the log-likelihood function. When comparing models, lower SSR is preferred. The minimized value is output in EViews and has no direct use, but is used as inputs in other diagnostics and used to compare between models. Sum of Squared Residuals (SSR): All the squared values of the residuals when using the estimated coefficients. No magic cut-off, but values less than 0.1 are viewed as strong evidence against irrelevance, while values less than 0.05 are viewed as very strong evidence against irrelevance. P-value of t-Stat The probability that the absolute value of the actual t-Stat is greater than the estimated t-Stat. Absolute t-stat values of 2 or more mean the 95% confidence interval of the coefficient does not include the value 0 But the greater the absolute value, the better. T-Statistic: Determines whether or not an independent variable is irrelevant to the regression (i.e.
Estimated coefficients +- 2 std error is the 95% confidence interval. error (of each independent variable): Indicates the likely sample variability (and hence reliability). I just wanted to know if my interpretation of the follow values were right:
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