After some more thought, I have a second answer on Tobi
Tobi Brütsch's <[email protected]> problem. To remind you,
> I have to match two datasets from two diffrend sources but about the same
> issue:
>
> Dataset1 (+/- 5000 observations):
> date Firm recommendation brokerID
> 15.03.2002 ABB 2 LEHM
> 11.01.2005 ABB 5 HESLB
> 01.07.2005 ABB 2 JBCOB
> 06.08.2004 ABB 3 MORGAN
>
> Dataset2 (+/- 2500 observations):
> Date firm brokerID recommendation
> 04aug2003 ABB MORGAN 4
> 13nov2002 ABB WAREURO 4
> 22mar2005 ABB JYSKE 4
> 25jan2005 ABB SOGENED 4
> 27jan2004 ABB PARIBEU 3
>
> [...]
> [...] i want to analyse if the recommendations of Dataset 2 are:
>
> 1. the same like these in set 1 (should be for some but I think not for
> all)
>
> 2. The recommendations in set 2 are published with a certain lag,
> perhabs 5-10 days after the same recommendation in set1.
> Now i don't know to merge the sets. [...]
My previous answer had to do with using -append- and Tobi forming the fuzzy
merge himself.
My second approach would use -merge- repeatedly.
The setup would be,
First,
. use Dataset1
. merge firm brokerID date recommendation using Dataset2
. count if _merge==3
Write that number down. That's the number of recommendations that are
in exact agreement between the two sources.
Next,
. use Dataset1, clear
. replace date = date + 1
. sort firm brokerID date recommendation
. merge firm brokerID date recommendation using Dataset2
. count if _merge==3
Write that number down. That's the number of recommendations that are
in agreement where source 2 made the same recommendation 1 day later.
Next,
. use Datqaset1, clear
. replace date = date - 1
. sort firm brokerID date recommendation
. merge firm brokerID date recommendation using Dataset2
. count if _merge==3
That's the number where source 2 made the same recommendation 1 day before.
Now keep doing that with date+2, date-2, and so on.
-- Bill
[email protected]
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