Dear All,
Thanks to all who have contributed to my query on FE
vs RE as means of addressing both unobserved effect
and endogeneity where regressors are less
time-variant. Some comments to your suggestions are in
order:
Kit Baum: Presence of more than one endogenous
variable complicates estimating the equation using
xtivreg2 (which is considered to be single equation
model). An alternative resort could be estimating the
equations using 3sls with reg3. However, I am not sure
if this 3sls is recommended if the number of equations
increases. Moreover, the equations in the system need
to be identified and hence require adjustment to make
the system identifiable! Comments on this issue will
be helpful.
Daniel Hoechle: I was not aware of this Driscoll-Kraay
estimation procedure. Thanks for introducing this to
me. Much appreciate if I can avail a copy of the macro
including 2sls estimator.
Rodrigo: I am novice in programming stuffs and hence
may not be able to follow your suggestion in adjusting
HT for including instrument at the third step. Yet, I
tried with the standard one with xthataylor but this
requires at least one time-invariant endogenous and
exogenous variable where I have only one endogenous
time-invariant variable. For your interest, I am
including the following information regarding my data
and results:
1.The value of R-square with FE estimation is: within
= 0.4727 , between = 0.0628 , and sigma u =
0.39716817, sigma e = .21452491 and rho = .7741.
2.My sample is constructed on the basis of top 300 US
firms at the end of 2004 and the data is unbalanced as
the firms merged or acquired over the sample period
1997 to 2004.
Further query: I shall highly appreciate any comments
on: to what extent pooled-OLS with cluster by firm id
option able to address unobserved effect.
Thank you once again for your time and understanding
and helpful comments. Have a nice week end!
Regards,
Shams
PhD candidate
Accounting and Finance Dept.
Monash University
--- Daniel Hoechle <[email protected]> wrote:
> Hi,
>
> This is a really interesting discussion. I think
> Rodrigo is right in
> saying that the standard Fama-MacBeth procedure is
> not appropriate
> here because of the endogeneity problem. However,
> Antoni Sureda
> provides a version of the Fama-MacBeth approach
> which is based on the
> IV-estimator rather than on the OLS estimator. His
> -fmivreg- program
> is available from
>
http://www.antonisureda.com/blog/files/category-6.html
>
> What could also be an interesting path to proceed is
> to estimate the
> regression model with Driscoll-Kraay standard
> errors. Why? If the
> panel is unbalanced then the panel might well be a
> microeconometric
> panel and these panels are likely to be
> cross-sectionally dependent
> (due to things like social norms, neighborhood
> effects, and all sorts
> of behavioral biases). Because Driscoll-Kraay
> standard errors are
> heteroscedasticity consistent and robust to very
> general forms of
> temporal and cross-sectional dependence, they might
> be interesting in
> this respect. I implemented the Driscoll-Kraay
> estimator for use with
> both balanced and unbalanced panels in my -xtscc-
> program (in Stata
> type -net search xtscc-). Unfortunately, however,
> the -xtscc- program
> currently does not allow for estimation of IV
> regression models but it
> would be straightforward to generalize the -xtscc-
> program such that
> it includes the 2SLS estimator. Please let me know
> if this would be of
> interest for anyone.
>
> Finally, if there is no cross-sectional dependence,
> then I think it
> could be a simple but tractable way to estimate the
> regression model
> by aid of the 2SLS estimator with panel-robust
> ("clustered" or Rogers)
> standard errors. Monte Carlo simulations have shown
> that panel-robust
> standard errors are robust to subject specific fixed
> effects.
>
> Best,
> Dan
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