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Re: st: Regression and Looping


From   Nick Cox <[email protected]>
To   "[email protected]" <[email protected]>
Subject   Re: st: Regression and Looping
Date   Sun, 2 Mar 2014 23:50:40 +0000

You need to tell us more about your dataset. Presumably you are not
trying to get regressions out of single observations....

You could try this on your original dataset.

gen OK = !missing(return_p, return_m)

tab dt OK

The variable -OK- will be 1 for every observation that could
participate in a regression.

Nick
[email protected]


On 2 March 2014 23:44, Reese Andorfer <[email protected]> wrote:
> Hi Nick,
>
> first of all thank you for the quick reply.
> I tried the statsby command now unfortunately do I get only missing
> values for a0 and a1 ....
>
> and this is basically what I get once I execute the command:
>
> Statsby groups
> ----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
> xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx    50
> xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx   100
> xxxxxxxxxxxxxxxxxxxx
>
> any idea what I could possibly do wrong here ?
>
> Thank you again,
> Best Reese
>
> 2014-03-02 23:20 GMT+00:00 Nick Cox <[email protected]>:
>> This sounds confused. -ID- as e.g. "01Jan03" could not be the result
>> of applying -string()- to a numeric variable unless you also specified
>> a date format. In any case, if you have an integer date variable, it
>> should not need conversion to string for this purpose.
>>
>> That said, this sounds like some misunderstanding of your data.
>>
>> I tend to use -statsby- for this kind of exercise. Here is a silly example:
>>
>> . webuse grunfeld, clear
>>
>> . statsby a0=_b[_cons] a1=_b[mvalue] , by(year) : regress invest mvalue
>> (running regress on estimation sample)
>>
>> In your case it sounds as if you just should try
>>
>> statsby a0=_b[_cons] a1=_b[return_m] , by(dt) : regress return_p return_m
>>
>> Nick
>> [email protected]
>>
>> On 2 March 2014 22:58, Reese Andorfer <[email protected]> wrote:
>>
>>> I have an industry portfolio with at the respective return return_p
>>> and then I the return of the market portfolio return_m. My time period
>>> is 10 years and I have the returns- on a monthly basis.
>>>
>>> Now I would like to perform the following regression : reg return_p
>>> return_m for every month in my 10 year time period.
>>>
>>> I would like to save the constant as well as the beta coefficient for
>>> return_m, thus I tried to use the following :
>>>
>>> gen ID=string( dt)
>>>
>>>
>>> gen a0=.
>>> gen a1=.
>>>
>>>
>>> levelsof ID, local(x)
>>> foreach obs of local x {
>>> reg return_p return_m if ID=="`obs'"
>>> replace a0=_b[_cons] if ID=="`obs'"
>>> replace a1=_b[return_m] if ID=="`obs'"
>>> }
>>>
>>>
>>> whereby ID - is the date of the observation f.e. 01Jan03 in string form
>>>
>>> now I have no missing observations whatsoever and I get the error no
>>> observations.
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