Hi all,
At the moment I am having problems creating an Event Study. I want to look at the influence of past weather on the hiring behavior of companies.
For this I am using weather shocks. Currently I have found the Eventdd command, but this can only use binary year dummies for years, but I need the effect of a continuous variable on hiring behavior, i.e. the interaction term between the year variable and the annual rain days variables.
My Data for 1 company looks like this:
I used the regression command:
where the Lags/Leads are a binary variable indicating the distance to the event and WeatherLag is a contionous variable for the weather that happened in that year. However, the results are not really realistic, where is my mistake here?
Thank you all in advance!
Jakob
At the moment I am having problems creating an Event Study. I want to look at the influence of past weather on the hiring behavior of companies.
For this I am using weather shocks. Currently I have found the Eventdd command, but this can only use binary year dummies for years, but I need the effect of a continuous variable on hiring behavior, i.e. the interaction term between the year variable and the annual rain days variables.
My Data for 1 company looks like this:
Employment | Weather | ID | WeatherLag_4 | Lag4 | WeatherLag_3 | Lag3 | WeatherLag_2 | Lag2 | WeatherLag_1 | Lag1 | WeatherLag_0 | Lag0 | WeatherLead_1 | Lead1 | WeatherLead_2 | Lead2 | WeatherLead_3 | Lead3 | WeatherLead_4 | Lead4 | WeatherLead_5 | Lead5 | year | Shock |
80 | 70 | 1 | 50 | 1 | 60 | 0 | 45 | 0 | 56 | 0 | 70 | 0 | 60 | 0 | 40 | 0 | 80 | 0 | 200 | 0 | 40 | 0 | 1989 | 1993 |
50 | 60 | 1 | 60 | 0 | 45 | 1 | 56 | 0 | 70 | 0 | 60 | 0 | 40 | 0 | 80 | 0 | 200 | 0 | 40 | 0 | 80 | 0 | 1990 | 1993 |
60 | 40 | 1 | 45 | 0 | 56 | 0 | 70 | 1 | 60 | 0 | 40 | 0 | 80 | 0 | 200 | 0 | 40 | 0 | 80 | 0 | 60 | 0 | 1991 | 1993 |
70 | 80 | 1 | 56 | 0 | 70 | 0 | 60 | 0 | 40 | 1 | 80 | 0 | 200 | 0 | 40 | 0 | 80 | 0 | 60 | 0 | 70 | 0 | 1992 | 1993 |
40 | 200 | 1 | 70 | 0 | 60 | 0 | 40 | 0 | 80 | 0 | 200 | 1 | 40 | 0 | 80 | 0 | 60 | 0 | 70 | 0 | 56 | 0 | 1993 | 1993 |
15 | 40 | 1 | 60 | 0 | 40 | 0 | 80 | 0 | 200 | 0 | 40 | 0 | 80 | 0 | 60 | 0 | 70 | 0 | 56 | 0 | 76 | 0 | 1994 | 1993 |
10 | 80 | 1 | 40 | 0 | 80 | 0 | 200 | 0 | 40 | 0 | 80 | 0 | 60 | 1 | 70 | 0 | 56 | 0 | 76 | 0 | 84 | 0 | 1995 | 1993 |
12 | 60 | 1 | 80 | 0 | 200 | 0 | 40 | 0 | 80 | 0 | 60 | 0 | 70 | 0 | 56 | 1 | 76 | 0 | 84 | 0 | 65 | 0 | 1996 | 1993 |
14 | 70 | 1 | 200 | 0 | 40 | 0 | 80 | 0 | 60 | 0 | 70 | 0 | 56 | 0 | 76 | 0 | 84 | 1 | 65 | 0 | 43 | 0 | 1997 | 1993 |
I used the regression command:
Code:
reghdfe employment Lag_5##WeatherLag_5 Lag_4##WeatherLag_4 Lag_3##WeatherLag_3 Lag_2##WeatherLag_2 Lag_1##WeatherLag_1 Lag_0##WeatherLag_0 Lead_1##WeatherLead_1 Lead_2##WeatherLead_2 Lead_3##WeatherLead_3 Lead_4##WeatherLead_4 Lead_5##WeatherLead_5 , absorb(i. year)
Thank you all in advance!
Jakob
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