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Posted 03-10-2019 12:44 AM
(1313 views)

I have below mentioned two models. In Model-1 dependent (Buy) and independent (Gender) variables are categorical variables. Rest of the control variables are continuous. Whereas, in Model-2 all the dependent and independent variables (i.e. interaction terms of gender and types of executives) are dummy variables. Also, I need to include **two-way fixed effect** in my regression models i.e. **time and firm fixed effect**. My data has 12 months and 4000 unique firms.

I am not sure * which "proc" to use for these two models* to get accurate estimates. Just to mention, for Model-2, I need to measure

**Model - 1:**

Buy= **α**+ **β****1**Gender+ **β2**Age+ **β3**Experience+ **β4**Return+ **β5**Firm_Size+ **β6**Volatility+ **β7**shares+ ∑ **β**month+ ∑ **β**firm+ **ε**

**Model - 2:**

Buy= **α**+ **β****1**Female_Chairman + **β2**Male_Chairman + **β3**Female_Director + **β4**Male_Director + **β5**Female_Officer + **β6**Male_Officer+ ∑ **β**month+ ∑ **β**firm+ **ε**

**Buy (dependent)** = dummy variable equals “1” when shares are bought, and “0” if shares are sold.

**Gender (independent)** = dummy variable equals “1” if executive is a female, and “0” if male.

Previously I used proc glm, but then realized that due to a binary dependent variable the coefficients would be misleading.

```
proc glm data= Model_1;
absorb firm;
class month;
model Buy = Gender Age Experience Return Firm_Size Volatility shares month / solution noint;
run;
```

Kindly suggest an appropriate syntax for the solution. Thanks

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Elsewhere I recall that you (@Saba1) say there are 4000+ firms. I'm guessing that will be too many for most procedures (might work in HPMIXED; @StatDave might know). And regardless, I wonder how you would interpret the effect of a fixed effects factor with over 4000 levels.

Would it be better to think of firm (PERMNO, elsewhere) as a random effects factor, where the 4000 firms in your dataset represent a random sample of all possible firms that you would want to make inference to? This would be a shift in how you envision your study and the questions it is meant to address.

I agree with @StatDave that you should use a procedure that is appropriate for a binary response, and that you should incorporate your categorical predictor variables in a form that can be use in the CLASS statement.

Other aspects of your study suggest that observations are not independent and consequently a mixed model may be necessary. A mixed model with a binary response is complicated; SAS® for Mixed Models: Introduction and Basic Applications is an excellent resource.

I hope this helps.

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@StatDave Thank you so much. I am using proc logistic for the mentioned models. I am a bit confused regarding the difference between strata and class statements. My purpose is to include time and firm fixed effects in the models i.e. creating dummies for each one of them and considering last category as reference. Class statement does the trick, i.e. putting both "Month" and "PERMNO" in class statement. I expect the same results when using PERMNO in Strata statement and Month in Class Statement, however, the results are different. Using PERMNO in Class statement is taking around 2 - 2.30 hours in creating output tables.

Need your suggestion in this regard. Thanks

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- Tags:
- fixed effects

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@StatDave: Thank you so much for this detailed explanation. I am very clear now about the workings of class and strata statements. Thanks again.

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