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RE: st: xtabond and OLS for separate years


From   Kate Ivanova <[email protected]>
To   [email protected]
Subject   RE: st: xtabond and OLS for separate years
Date   Wed, 05 May 2004 00:48:52 -0700

Thank you very much again, Mark and Antonio. I really appreciate all your
comments. 

Now I could compare my OLS in cross section to between estimator, fixed
effects, first differences [thanks to your help] and random effects. My
income and income squared are significant in all OLS cross sections for 10
years. They are also significant in the between-effects, random-effects and
fixed effects models. However, the signs on all my coefficients are wrong in
the fixed effects estimation. In the random effects model, the sign of the
coefficient on my third variable, corruption, is wrong (though significant).
So I am basically left with the between estimator as it seems that I do not
have enough "within" variation in my explanatory variables. Does that mean
that I should not use xtabond or is it possible to get round this problem?

As for the first-differenced results, only two of the variables are barely
significant (at the 10% level) but the signs are wrong for both of them.

Please let me know if you have any suggestions.

Kate

-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Antonio Rodrigues
Andres
Sent: Tuesday, May 04, 2004 5:12 PM
To: [email protected]
Subject: RE: st: xtabond and OLS for separate years

xtivreg using as instruments the same regressors gives the same result
that doing it by hand

Yes, you get the same results.


 *FIRST DIFFERENCES
. xtivreg lsrt ($xvars=income unempl), fd

First-differenced IV regression                 Number of obs      =    
  276
Group variable: country                         Number of groups   =    
   21

R-sq:  within  = 0.0096                         Obs per group: min =    
    3
       between = 0.8987                                        avg =    
 16.9
       overall = 0.3945                                        max =    
   37

                                                chi2(2)            =    
 4.12
corr(u_i, Xb)  = -0.5904                        Prob > chi2        =   
0.1277

----------------------------------------------------------------------------
--
d.lsrt       |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
income       |
          D1 |  -.0176139   .0088856    -1.98   0.047    -.0350292  
-.0001985
unempl       |
          D1 |  -.0041495   .0044982    -0.92   0.356    -.0129659   
.0046669
_cons        |    .005815    .005735     1.01   0.311    -.0054254   
.0170553
-------------+--------------------------------------------------------------
--
     sigma_u |  .42097985
     sigma_e |  .05951118
         rho |  .98040789   (fraction of variance due to u_i)
----------------------------------------------------------------------------
--
Instrumented:   income unempl
Instruments:     income unempl

. regress dlsrt dincome dunempl

      Source |       SS       df       MS              Number of obs =  
  276
-------------+------------------------------           F(  2,   273) =  
 2.06
       Model |  .014576629     2  .007288314           Prob > F      = 
0.1297
    Residual |  .966851358   273   .00354158           R-squared     = 
0.0149
-------------+------------------------------           Adj R-squared = 
0.0076
       Total |  .981427987   275  .003568829           Root MSE      = 
.05951

----------------------------------------------------------------------------
--
       dlsrt |      Coef.   Std. Err.      t    P>|t|     [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
     dincome |  -.0176139   .0088856    -1.98   0.048    -.0351068  
-.0001209
     dunempl |  -.0041495   .0044982    -0.92   0.357    -.0130051   
.0047061
       _cons |    .005815    .005735     1.01   0.312    -.0054755   
.0171054
----------------------------------------------------------------------------
--



>>> [email protected] 05/05/04 12:27 AM >>>
Kate,

Quoting Kate Ivanova <[email protected]>:

> Hi Mark,
> 
> I tried to run xtreg, fd (first-differencing without instrumenting)
> as you
> suggested but I could not find this option in the xtreg command.
> There is
> xtivreg, fd but then this is for estimation with instrumental
> variables. Is
> there any other way to estimate a first-differenced model without
> instrumenting?

Funny, I thought it would be there.

I suppose you either have to try to trick xtivreg into running an 
uninstrumented equation by specifying the same instruments as regressors
- 
and it might be too clever to be fooled - or you have to first
difference 
by hand.

--Mark

> Thanks!
> 
> Kate
> 
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Kate
> Ivanova
> Sent: Monday, May 03, 2004 10:02 AM
> To: [email protected]
> Subject: RE: st: xtabond and OLS for separate years
> 
> Mark,
> 
> Thank you very much for your suggestions. They are very helpful.
> Yes, I did
> mean OLS in cross-section so I'll now compare it to the estimators
> you
> specified below. I'll get back again when I have the results. Thanks
> a lot! 
> 
> Kate
> 
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Mark
> Schaffer
> Sent: Monday, May 03, 2004 4:09 AM
> To: [email protected]
> Subject: Re: st: xtabond and OLS for separate years
> 
> Kate,
> 
> Date sent:      	Sat, 01 May 2004 17:59:00 -0700
> From:           	Kate Ivanova <[email protected]>
> Subject:        	st: xtabond and OLS for separate years
> To:             	[email protected]
> Send reply to:  	[email protected]
> 
> > Hi,
> > 
> > I am confused by the results I get using xtabond. I have a panel
> of 116
> > countries over 10 years and when I run separate regressions for
> each year
> > using OLS, my variables (income and income squared) are highly
> significant
> > (at a 0.001 level). But when I run xtabond with one lag of the
> dependent
> > variable, they are not significant at all. I wonder why I have
> such a
> > difference between the results. Any help, any ideas would be
> greatly
> > appreciated.
> 
> It's hard to tell exactly what's going on from the info you've 
> provided, but there are at least two possibilities:
> 
> 1. You are comparing OLS in cross-section (yes?) and xtabond, which
> 
> you can think of as a first-difference estimator with instrumenting.
>  
> Each of your cross-sections uses the cross-sectional variation
> across 
> 116 countries in any year; xtabond using only the time-series 
> variation within countries.
> 
> A better comparison would be to leave the instrumenting out of it
> for 
> the moment, and compare:
> 
> OLS period-by-period (uses only cross-sectional variation)
> Between estimator (also uses only cross-sectional variation)
> Fixed effects (uses only "within", i.e., time-series, variation)
> First differences (also uses only "within" variation)
> Random effects (uses both "within" and "between" variation)
> 
> 2.  xtabond is an IV estimator, and the results you get will depend
> 
> on the instrumenting.  You can compare the xtabond results with the
> 
> results from first-differencing without instrumenting (xtreg, fd),
> 
> for example, and see what happens.
> 
> Hope this helps.
> 
> --Mark
> 
> > 
> > Kate
> > 
> > 
> 
> 
> Prof. Mark E. Schaffer
> Director
> Centre for Economic Reform and Transformation
> Department of Economics
> School of Management & Languages
> Heriot-Watt University, Edinburgh EH14 4AS  UK
> 44-131-451-3494 direct
> 44-131-451-3008 fax
> 44-131-451-3485 CERT administrator
> http://www.som.hw.ac.uk/cert
> *
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Prof. Mark Schaffer
Director, CERT
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS
tel +44-131-451-3494 / fax +44-131-451-3008
email: [email protected]
web: http://www.sml.hw.ac.uk/ecomes
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