this as the same as you would get from sum bought if saidhi = 0 & sign = 0). Your constant is represents the average value of bought when both are 0 (e.g. keep in mind the total effect of them both being one includes the previous two terms). the marginal effect of them both being 1 at the same time. The parts under saidhi#sign describe the interaction between these two variables (i.e. 1.sign is the effect of the sign, alone, i.e. Hausman test digunakan untuk menentukan apakah data panel yang diperoleh sebaiknya diproses dengan menggunakan fixed effect atau random effect. 210873ДЎ.saidhi is the effect of saidhi when sign = 0. My dependent variable is a dummy that is 1 if a customer bought something and 0 if not. I have a bunch of dummy variables that I am doing regression with. The data here is made up, but bear with me. Your output should be: Linear regression Number of obs = 100Д«ought | Coef. Very new to Stata, so struggling a bit with using fixed effects. If we donвЂ™t have too many fixed-effects, that is to say the total number of fixed-effects and other covariates is less than Statas maximum matrix size of 800, and then we can just use indicator variables for the fixed effects.

Now, run your regression: * y = x + FE + x*FE + cons I cannot see that it is possible to do it directly in Stata. Gen sign = runiform() + runiform()*saidhi + runiform()*bought > 0.66666 // Binary FE, correlated with both x and y Allison says In a fixed effects model, the unobserved variables are allowed to have any associations whatsoever with the observed variables. Gen saidhi = runiform() + runiform()^2*bought Random effects models will estimate the effects of time-invariant variables, but the estimates may be biased because we are not controlling for omitted variables. Gen bought = runiform() > 0.5 // Binary y, 50/50 probability saidhi should be correlated with your outcome (so there is a portion of saidhi that is uncorrelated with bought and a portion that is), and your FE variable should be correlated with both bought and saidhi (otherwise there is no point having it in your regression if you are only interested in the effect of saidhi).

More explicitly, you might do something like: xtset industry xtreg y x1 x2 i.

Call xtreg with the fe option to indicate fixed effects, including the dummy variables for year as right hand side variables. You can use the # operator in a regression to get a saturated model with fixed effects:ДЇirst, input data such that you have a binary outcome ( bought), a dependent variable ( saidhi), and a fixed effects variable ( sign). use xtset industryvar in Stata to indicate you want fixed effects for each unique value of industryvar.

Fixed effects stata