Hello!
I want to analyse how a market entry of a specific app is associated with a change in the download size (in bytes) of competing apps. I did a panel DiD with a control group containing app data of unaffected apps and in the treatment group all affected apps. At period >=4, the treatment indicator takes on the value 1, otherwise 0. First I did a graphical inspection to test the common trend assumption. It looked like there is a negative effect on the download size of affected apps (as expected). After I did the regression with stata, I got a significant postive effect. How is this possible? Im also concerened about the fact that stata is omitting the treatment indicator in the output. I did the same analysis with another app where stata didnt omit the treatment indicator.
Did I make a mistake and can someone help me to understand how the grafical trend inspection deviates so much from the estimation?
This is the graph (x-axis time, y-axis download_size)
Thank you and all the best
Vince

Graph: Array


Output:
. xtreg download_size i.post##i.treatment age, cl(application_id) fe
note: 1.treatment omitted because of collinearity

Fixed-effects (within) regression Number of obs = 172,875
Group variable: applicatio~d Number of groups = 49,617

R-sq: Obs per group:
within = 0.0023 min = 1
between = 0.0135 avg = 3.5
overall = 0.0102 max = 6

F(3,49616) = 40.99
corr(u_i, Xb) = -0.1156 Prob > F = 0.0000

(Std. Err. adjusted for 49,617 clusters in application_id)
--------------------------------------------------------------------------------
| Robust
download_size | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------+----------------------------------------------------------------
1.post | -305664.7 148492.5 -2.06 0.040 -596711.9 -14617.6
1.treatment | 0 (omitted)
|
post#treatment |
1 1 | 9196838 1955476 4.70 0.000 5364082 1.30e+07
|
age | 3463.04 366.4428 9.45 0.000 2744.807 4181.272
_cons | 9.13e+07 313649.8 291.22 0.000 9.07e+07 9.20e+07
---------------+----------------------------------------------------------------
sigma_u | 1.542e+08
sigma_e | 19961402
rho | .98351201 (fraction of variance due to u_i)
--------------------------------------------------------------------------------




Output (log specifiaction):
. xtreg lsize i.post##i.treatment age, cl(application_id) fe
note: 1.treatment omitted because of collinearity

Fixed-effects (within) regression Number of obs = 172,875
Group variable: applicatio~d Number of groups = 49,617

R-sq: Obs per group:
within = 0.0145 min = 1
between = 0.0597 avg = 3.5
overall = 0.0431 max = 6

F(3,49616) = 131.86
corr(u_i, Xb) = -0.2236 Prob > F = 0.0000

(Std. Err. adjusted for 49,617 clusters in application_id)
--------------------------------------------------------------------------------
| Robust
lsize | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------+----------------------------------------------------------------
1.post | -.0008987 .0006355 -1.41 0.157 -.0021442 .0003469
1.treatment | 0 (omitted)
|
post#treatment |
1 1 | .1757632 .0272084 6.46 0.000 .1224344 .2290919
|
age | .000027 1.55e-06 17.40 0.000 .0000239 .00003
_cons | 17.69652 .0013271 1.3e+04 0.000 17.69392 17.69912
---------------+----------------------------------------------------------------
sigma_u | 1.1514444
sigma_e | .07930105
rho | .99527919 (fraction of variance due to u_i)
--------------------------------------------------------------------------------