I have the following model:

$y_t = \alpha + \beta_1 x_{t-1} + \beta_2 z_{t-1} + \varepsilon_t$,

where my dependent variable $y_t$ is the log return of a stock (e.g., GM) and $x_{t-1}$ and $z_{t-1}$ are dummy variables. I have three possible categories (positive, negative and neutral news in the pre-period). However to avoid collinearity I code only two dummies (positive and negative news). Thus, the reference category are neutral news appeared in the pre-period.

My question refers to the interpretation of the results and especially the intercept. E.g., if I get as results from the OLS estimation of the model above:

$\alpha$ = -0.028 (t-stat. = -1.91)

$\beta_1$ = 0.024 (t-stat. = 1.76)

$\beta_2$ = -0.002 (t-stat. = -0.60)

My question is: Can I interpret the intercept in the same way as the beta-coefficients, i.e., -0.028 is the expected mean return for neutral news, 0.024 the expected return for positive news and -0.002 is the expected mean for negative news?

Thanks for your help!


Your interpretation of $\alpha$ is correct. However $\beta_1$ is the difference between the expected return for positive news and the expected return for neutral news. That is because for for postive news you have $x_{t-1}=1$ and $z_{t-1}=0$. This means you the prediction for positive news is $\alpha + \beta_1$.

  • $\begingroup$ Would it be correct to say for Beta 1 that it indicates that the return for positive news is 0.024 higher than the return for neutral news? $\endgroup$ – jeffrey Oct 7 '15 at 6:55
  • $\begingroup$ Yes. But note that relatively high standard error of 0.014 should be part of the interpretation. $\endgroup$ – Erik Oct 7 '15 at 7:10

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