I am not sure about my interpretation of the t-ratios in dummy regression models for event studies. I have the results for two different groups of models examining the impact of news on stock returns and I want to compare them.
The first group applies the following model:
(1) $R_{t}=\beta_{0} + \beta_{1}R_{mt}+\beta_{2}D_{Gt}+\beta_{3}D_{Bt}+\epsilon_{t}$
where ${R}_{t}$ is the return of a company at time t, $R_{mt}$ is the market return at time t, $D_{Gt}$ is a dummy variable that equals one in the event window of Good News occurring and $D_{Bt}$ is a similar dummy indicating the occurrence of bad news. Thus, the coefficient $\beta_{2}$ ($\beta_{3}$) signals the abnormal returns after good (bad) news.
The second group includes just a dummy for good news:
(2) $R_{t}=\beta_{0} + \beta_{1}R_{mt}+\beta_{4}D_{Gt}+\epsilon_{t}$
here $D_{Gt}$ is equal to one if good news occur and 0 if bad news occur. Thus, $\beta_{4}$ shows the difference in the returns after good news in comparison to bad news.
My question is: How to get the absolute abnormal returns for good and bad news from model type 2? Is the abnormal return after good news $\beta_{0}+\beta_{4}$? And if $\beta_{4}$ has a t-value of 3.00, can I say that the t-value of $\beta_{0}+\beta_{4}$ is also 3.00 and thus the abnormal returns after good news are statistically significant?
Thanks for your help!