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# Re: st: conception confusion - "fixed effects" and time effect on data with time factor

 From Nick Cox To statalist@hsphsun2.harvard.edu Subject Re: st: conception confusion - "fixed effects" and time effect on data with time factor Date Fri, 21 Oct 2011 08:42:56 +0100

```Your related question looks like the same question to me. As Maarten
explained in detail, you need to think out in relation to your
research problem what time implies. No outsider, expert or not, can be
expected to have a useful opinion on what is "OK" for your problem.

Nick

On Thu, Oct 20, 2011 at 9:50 PM, House Wang <jwanghouse@gmail.com> wrote:
> Maarten,
>
> Thanks for your the reminder, especially this sentence "you would need
> to think really hard whether the variables you are by proxy
> controlling for are in fact intervening or confounding variables."
> Yes, I think the the variable I am by proxy controlling for is in fact
> intervening dependent variables.
>
> I have a related question. Is it O.K. that I directly add year as a
> variable in the model, instead of i.year?
>
> Thanks.
> Jianying
>
> On Wed, Oct 19, 2011 at 2:35 AM, Maarten Buis <maartenlbuis@gmail.com> wrote:
>> On Tue, Oct 18, 2011 at 7:58 PM, House Wang wrote:
>>> In this study, I am interested in the random effects of the year
>>> variable, which means the errors of D.V. due to year.
>>> To measure the random effects of year variable, the correct Stata
>>> command is to add i.year to the model, right?
>>
>> There is some inconsistent terminology in your question. I guess you
>> want to do a regular regression (-regress-) and you want to know how
>> much of the variance in your dependent/explained/outcome/y variable is
>> explained by time.
>>
>> The correct parametrization of time depends on how you think time
>> "works" in your problem. Time can have an effect on your outcome in
>> the sense of aging or decay or it can be a proxy for everything that
>> happened in a given year that in turn may have influenced your outcome
>> variable. In the former case you'd probably want some kind
>> (non-linear) trend, while in the latter case you probably want to add
>> it as a categorical variable like you suggested. However, you would
>> need to think really hard whether the variables you are by proxy
>> controlling for are in fact intervening or confounding variables. In
>> the former case you'll bias your results when you include time and in
>> the latter case you'll bias your results when you don't include time.
>> The problem is that probably some of the variables are intervening and
>> others confounding. I that case the best, but probably unrealistic,
>> solution is to directly measure the variables you want to control for
>> rather than rely on a proxy.
>>
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