Advantages over the Chow test:

The dummy variable technique requires running only a single equation while for the chow test one has to run a number of regressions, one each for each of the periods concerned.

The chow test does not tell us whether the intercept or the slope coefficient or both  are different in  the  two periods.  In fact we  cannot tell by  the chow test which of the four possibilities represented by exists. In  this respect the use of dummy variables approach has a definite advantage as it not only tells whether the two regressions are different but also pinpoints the source of their difference, whether it is because of the intercept or slope coefficient or both.

The use of single regression model  in  case of  dummy variable  approach would  mean  higher  degrees  of  freedom  vis-a-vis more than  one regression models (in case of chow test) thereby improving  the relative precision  of the estimated parameters (but  it  should be  borne  in  mind  that  every  additional dummy variable used will also consume one extra degree of freedom).