Dear Statalist Community,
I am a newbie and (not yet
) an expert since this is my first time dealing with quantitative statistics. So if I express myself a little bit unclear or with wrong expressions: I am sorry for that! 
I am working on a student paper and I thought that for my topic, DiD might be a suitable method.
The context is the following: In general I want to analyse if a city which is host for an international event, this host city will profit from creating higher real estate value thereby.
So my treatment group is the host city, my control group is a comparable city with some similar macroeconomic numbers like population etc.
In detail, i want to analyse if certain treatment periods cause higher real estate values in comparison to the control group during that time. Those treatment periods are the bidding , announcement and post-event.
I have created the following table for this and would be very pleased if someone could give me feedback on this.
my command is reg valuehouses Host Bidding Announcement Post BxH AxH PxH
Is that correct?
Furthermore I am interested to learn how DiD would work with multiple outcome variables, e. g. valueappartments. I have not yet really understood how to translate that into a command now do I know if my current command is really right. And I am also not sure if this makes sense.
So... thank you in advance for any advice or comment - will do my best understand and transform it.
Regards,
Kristina
. reg valuehouses Host Bidding Announcement Post BxH AxH PxH
Source | SS df MS Number of obs = 62
-------------+---------------------------------- F(7, 54) = 25.42
Model | 26186.6417 7 3740.94881 Prob > F = 0.0000
Residual | 7948.41367 54 147.192846 R-squared = 0.7671
-------------+---------------------------------- Adj R-squared = 0.7370
Total | 34135.0553 61 559.591071 Root MSE = 12.132
------------------------------------------------------------------------------
valuehouses | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
Host | 6.136154 4.758682 1.29 0.203 -3.404429 15.67674
Bidding | -5.963248 8.006485 -0.74 0.460 -22.01528 10.08879
Announcement | 2.67 7.759849 0.34 0.732 -12.88756 18.22756
Post | 31.36667 5.78385 5.42 0.000 19.77075 42.96259
BxH | 12.7044 12.20749 1.04 0.303 -11.77014 37.17894
AxH | 1.720714 12.44344 0.14 0.891 -23.22688 26.66831
PxH | 10.49762 8.416367 1.25 0.218 -6.37618 27.37142
_cons | 107.2677 3.364896 31.88 0.000 100.5215 114.0139
I am a newbie and (not yet
) an expert since this is my first time dealing with quantitative statistics. So if I express myself a little bit unclear or with wrong expressions: I am sorry for that! I am working on a student paper and I thought that for my topic, DiD might be a suitable method.
The context is the following: In general I want to analyse if a city which is host for an international event, this host city will profit from creating higher real estate value thereby.
So my treatment group is the host city, my control group is a comparable city with some similar macroeconomic numbers like population etc.
In detail, i want to analyse if certain treatment periods cause higher real estate values in comparison to the control group during that time. Those treatment periods are the bidding , announcement and post-event.
I have created the following table for this and would be very pleased if someone could give me feedback on this.
my command is reg valuehouses Host Bidding Announcement Post BxH AxH PxH
Is that correct?
Furthermore I am interested to learn how DiD would work with multiple outcome variables, e. g. valueappartments. I have not yet really understood how to translate that into a command now do I know if my current command is really right. And I am also not sure if this makes sense.
So... thank you in advance for any advice or comment - will do my best understand and transform it.
Regards,
Kristina
| Host | Bidding | B x H | Announcement | A x H | Post | PxH | valuehouses |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 100,00 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 109,09 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 112,12 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 118,18 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 115,15 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 112,12 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 109,09 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 118,18 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 118,18 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 109,09 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 109,09 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 115,15 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 115,57 |
| 1 | 1 | 1 | 0 | 0 | 0 | 0 | 118,54 |
| 1 | 1 | 1 | 0 | 0 | 0 | 0 | 118,54 |
| 1 | 1 | 1 | 1 | 1 | 0 | 0 | 112,61 |
| 1 | 1 | 1 | 1 | 1 | 0 | 0 | 112,61 |
| 1 | 1 | 1 | 1 | 1 | 0 | 0 | 112,61 |
| 1 | 1 | 1 | 1 | 1 | 0 | 0 | 103,72 |
| 1 | 1 | 1 | 1 | 1 | 0 | 0 | 106,68 |
| 1 | 1 | 1 | 1 | 1 | 0 | 0 | 109,65 |
| 1 | 1 | 1 | 1 | 1 | 0 | 0 | 112,61 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 124,46 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 139,28 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 148,17 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 160,02 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 180,77 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 207,44 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 219,29 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 225,22 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 231,14 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100,00 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 103,23 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 106,45 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 109,68 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 116,13 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 112,90 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 109,68 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 106,45 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 103,23 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100,00 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 103,23 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 103,23 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100,95 |
| 0 | 1 | 0 | 0 | 0 | 0 | 0 | 104,10 |
| 0 | 1 | 0 | 0 | 0 | 0 | 0 | 107,26 |
| 0 | 1 | 0 | 1 | 0 | 0 | 0 | 104,10 |
| 0 | 1 | 0 | 1 | 0 | 0 | 0 | 104,10 |
| 0 | 1 | 0 | 1 | 0 | 0 | 0 | 100,95 |
| 0 | 1 | 0 | 1 | 0 | 0 | 0 | 107,26 |
| 0 | 1 | 0 | 1 | 0 | 0 | 0 | 110,41 |
| 0 | 1 | 0 | 1 | 0 | 0 | 0 | 107,26 |
| 0 | 1 | 0 | 1 | 0 | 0 | 0 | 107,26 |
| 0 | 1 | 0 | 0 | 0 | 1 | 0 | 116,72 |
| 0 | 1 | 0 | 1 | 0 | 1 | 0 | 126,18 |
| 0 | 1 | 0 | 1 | 0 | 1 | 0 | 129,34 |
| 0 | 1 | 0 | 1 | 0 | 1 | 0 | 132,49 |
| 0 | 1 | 0 | 1 | 0 | 1 | 0 | 145,11 |
| 0 | 1 | 0 | 1 | 0 | 1 | 0 | 157,73 |
| 0 | 1 | 0 | 1 | 0 | 1 | 0 | 170,35 |
| 0 | 1 | 0 | 1 | 0 | 1 | 0 | 173,50 |
| 0 | 1 | 0 | 1 | 0 | 1 | 0 | 179,81 |
. reg valuehouses Host Bidding Announcement Post BxH AxH PxH
Source | SS df MS Number of obs = 62
-------------+---------------------------------- F(7, 54) = 25.42
Model | 26186.6417 7 3740.94881 Prob > F = 0.0000
Residual | 7948.41367 54 147.192846 R-squared = 0.7671
-------------+---------------------------------- Adj R-squared = 0.7370
Total | 34135.0553 61 559.591071 Root MSE = 12.132
------------------------------------------------------------------------------
valuehouses | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
Host | 6.136154 4.758682 1.29 0.203 -3.404429 15.67674
Bidding | -5.963248 8.006485 -0.74 0.460 -22.01528 10.08879
Announcement | 2.67 7.759849 0.34 0.732 -12.88756 18.22756
Post | 31.36667 5.78385 5.42 0.000 19.77075 42.96259
BxH | 12.7044 12.20749 1.04 0.303 -11.77014 37.17894
AxH | 1.720714 12.44344 0.14 0.891 -23.22688 26.66831
PxH | 10.49762 8.416367 1.25 0.218 -6.37618 27.37142
_cons | 107.2677 3.364896 31.88 0.000 100.5215 114.0139
