st: Re: re: time trend or year effect for pooled data

["sebastian.blesse" <sebastianblesse2404@gmail.com>]

Dear all,

Prof. Baum said 
"In a pooled setting, I would include time fixed effects (i.e. i.year in
factor-variable notation) which will estimate a coefficient for each year.
This set of variables will absorb all time-specific (or "macro') variation. 

If you use instead a time trend, it does not matter whether it starts from 1
or starts from 1990; any variable for which D.time is a constant will yield
the same results, in terms of explanatory power. But using a linear time
trend constrains the time-effect coefficients to lie on a straight line,
whereas estimating i.time allows the coefficient pattern over years to be
whatever the data chooses. If you have ten years, it is a difference between
estimating nine coefficients and one coefficient. Are those eight
constraints accepted by the data? That is an easily testable hypothesis". 

As far as i got it, one would introduce yearly fixed effects for example in
a Difference in Difference set up (fixed effects regression) to capture for
common macro shocks. Yearly fixed effects are here dummies from t=2,..., T
for t=1,..., T. Can i set a time trend equivalently for t=2,...,T or is the
time trend defined as t=1,..,T.

Kind regards,

Sebastian Blesse
Christopher F Baum wrote
> <>
> Yan said
> 
>> 1)	I have a equation as this: y=a+b1*X1+b2*X2+b3*X3+...+ c*T +
>> error, where a, b, c are coefficients;
>> 2)	Y is a couple of dependent variables, which could be binary or
>> continuous;
>> 3)	T is a time trend and I use it to capture year effect;
>> 4)	My observation is user groups which were visited in different
>> years and I pool them together, treating them as cross-sectional data.
>> 
>> My question: how should I treat T? Should I value it as 1, 2, 3, ..., OR
>> just yearly (eg., 1990, 1991, 1992, ....). I run regressions (both
>> Probit and OLS) using both methods, and the regression results give me
>> different coefficients ad t statistics  for "T". 
>> 
>> Could anyone explain why and which method is appropriate for pooled
>> data?
> 
> In a pooled setting, I would include time fixed effects (i.e. i.year in
> factor-variable notation) which will estimate a coefficient for each year.
> This set of variables will absorb all time-specific (or "macro')
> variation.
> 
> If you use instead a time trend, it does not matter whether it starts from
> 1 or starts from 1990; any variable for which D.time is a constant will
> yield the same results, in terms of explanatory power. But using a linear
> time trend constrains the time-effect coefficients to lie on a straight
> line, whereas estimating i.time allows the coefficient pattern over years
> to be whatever the data chooses. If you have ten years, it is a difference
> between estimating nine coefficients and one coefficient. Are those eight
> constraints accepted by the data? That is an easily testable hypothesis.
> 
> Kit Baum   |   Boston College Economics & DIW Berlin   |  
> http://ideas.repec.org/e/pba1.html
>                               An Introduction to Stata Programming  |  
> http://www.stata-press.com/books/isp.html
>    An Introduction to Modern Econometrics Using Stata  |  
> http://www.stata-press.com/books/imeus.html
> 
> 
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/


Christopher F Baum wrote
> <>
> Yan said
> 
>> 1)	I have a equation as this: y=a+b1*X1+b2*X2+b3*X3+...+ c*T +
>> error, where a, b, c are coefficients;
>> 2)	Y is a couple of dependent variables, which could be binary or
>> continuous;
>> 3)	T is a time trend and I use it to capture year effect;
>> 4)	My observation is user groups which were visited in different
>> years and I pool them together, treating them as cross-sectional data.
>> 
>> My question: how should I treat T? Should I value it as 1, 2, 3, ..., OR
>> just yearly (eg., 1990, 1991, 1992, ....). I run regressions (both
>> Probit and OLS) using both methods, and the regression results give me
>> different coefficients ad t statistics  for "T". 
>> 
>> Could anyone explain why and which method is appropriate for pooled
>> data?
> 
> In a pooled setting, I would include time fixed effects (i.e. i.year in
> factor-variable notation) which will estimate a coefficient for each year.
> This set of variables will absorb all time-specific (or "macro')
> variation.
> 
> If you use instead a time trend, it does not matter whether it starts from
> 1 or starts from 1990; any variable for which D.time is a constant will
> yield the same results, in terms of explanatory power. But using a linear
> time trend constrains the time-effect coefficients to lie on a straight
> line, whereas estimating i.time allows the coefficient pattern over years
> to be whatever the data chooses. If you have ten years, it is a difference
> between estimating nine coefficients and one coefficient. Are those eight
> constraints accepted by the data? That is an easily testable hypothesis.
> 
> Kit Baum   |   Boston College Economics & DIW Berlin   |  
> http://ideas.repec.org/e/pba1.html
>                               An Introduction to Stata Programming  |  
> http://www.stata-press.com/books/isp.html
>    An Introduction to Modern Econometrics Using Stata  |  
> http://www.stata-press.com/books/imeus.html
> 
> 
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/





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