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RE: st: RE: First stage of panel IV


From   "Filippos Petroulakis" <[email protected]>
To   <[email protected]>
Subject   RE: st: RE: First stage of panel IV
Date   Tue, 12 Jun 2012 19:14:38 -0400

Hi Mark,

1) I have version 01.0.13 for xtivreg2, 03.0.08 for ivreg2, and 01.3.01 for ranktest

2) I get the exact same results. In fact I run both and create a variable equal to e(sample) for each and they are identifcal

3) You write

>It sounds like that's because the instrumenting of either w or q is
>weak.  But that's what the Angrist-Pischke F-stats are for.  If the AP
>F-stat for the regressor of interest is respectable, then you're OK.

But I'm not instrumenting for them. The problem arises when I merely put them in the regression so they are included in the first stage of x on z and the other covariates (so that they become included instruments). 

Thanks again,

Filippos

>>> "Schaffer, Mark E" <[email protected]> 06/12/12 4:36 AM >>>
Filippos,

> -----Original Message-----
> From: [email protected] 
> [mailto:[email protected]] On Behalf Of 
> Filippos Petroulakis
> Sent: 12 June 2012 04:11
> To: [email protected]
> Subject: Re: st: RE: First stage of panel IV
> 
> Hi Mark and thanks for your response. I am using Stata 11 and 
> the up to date version of xtivreg2.

Can you check/report to us your versions of xtivreg2, ivreg2 and
ranktest?

> I'll start another list 
> as it's getting too crowded
> 
> 1) I  was probably mistaken about this. Just to make sure, is 
> running OLS on a panel with all variables differenced 
> identical to running first differences?

Should be the case.

> 2) About xtivreg versus xtivreg2, I am certain and to make 
> sure I run xtivreg2 and then copy the code and then just 
> remove the 2. For xtivreg2 the first stage output is
> 
> 
> .   
> .   xtivreg2 y ( x =z)  ///
> l.bzo  ln_tunder15 ln_t15to24 ln_t25to44 ln_t45to64 
> ln_t65plus ln_fraction_male ln_pop_hisp_all 
> ln_pop_nh_black ln_pop_nh_white, fd   small first robust
> 
> FIRST DIFFERENCES ESTIMATION
> ----------------------------
> Number of groups =      1026                    Obs per 
> group: min =         1
>                                                               
>  avg =       1.9
>                                                               
>  max =         2
> 
> First-stage regressions
> -----------------------
> 
> First-stage regression of D.ln_stim_forfd:
> 
> OLS estimation
> --------------
> 
> Estimates efficient for homoskedasticity only Statistics 
> robust to heteroskedasticity
> 
>                                                       Number 
> of obs =     1928
>                                                       F( 11,  
> 1916) =    53.82
>                                                       Prob > 
> F      =   0.0000
> Total (centered) SS     =  1631.878852                
> Centered R2   =   0.2035
> Total (uncentered) SS   =  59641.31821                
> Uncentered R2 =   0.9782
> Residual SS             =  1299.720342                Root 
> MSE      =    .8236
> 
> --------------------------------------------------------------
> ----------------
> D.           |               Robust
> x |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
> -------------+------------------------------------------------
> ----------
> -------------+------
>     ln_bzo |
>          LD. |  -.1256429   .0767888    -1.64   0.102    
> -.2762413    .0249555
>              |
>  ln_tunder15 |
>          D1. |   5.698369   3.323788     1.71   0.087    
> -.8202531    12.21699
>              |
>   ln_t15to24 |
>          D1. |   6.831107   2.217364     3.08   0.002     
> 2.482406    11.17981
>              |
>   ln_t25to44 |
>          D1. |   32.90151   3.672888     8.96   0.000     
> 25.69823    40.10479
>              |
>   ln_t45to64 |
>          D1. |   8.227723   3.417776     2.41   0.016     
> 1.524771    14.93068
>              |
>   ln_t65plus |
>          D1. |    4.88607   2.705773     1.81   0.071    
> -.4205002    10.19264
>              |
> ln_fractio~e |
>          D1. |   -3.86913    9.62245    -0.40   0.688    
> -22.74071    15.00245
>              |
> ln_pop_his~l |
>          D1. |  -6.352615   1.729689    -3.67   0.000    
> -9.744887   -2.960343
>              |
> ln_pop_nh_~k |
>          D1. |   7.426661   3.028961     2.45   0.014     
> 1.486254    13.36707
>              |
> ln_pop_nh_~e |
>          D1. |  -5.481275   3.728017    -1.47   0.142    
> -12.79267    1.830123
>              |
> z |
>          D1. |   3.404714   .2067276    16.47   0.000     
> 2.999279    3.810149
>              |
>        _cons |   5.682521   .0508073   111.84   0.000     
> 5.582877    5.782164
> --------------------------------------------------------------
> ----------------
> 
> 
> With xtivreg it is
> 
> 
> .   xtivreg y ( x =z)  ///
> l.ln_crime  ln_tunder15 ln_t15to24 ln_t25to44 ln_t45to64 
> ln_t65plus ln_fraction_male ln_pop_hisp_all 
> ln_pop_nh_black ln_pop_nh_white, fd   small first
> 
> First-stage first-differenced regression
> 
>       Source |       SS       df       MS              Number 
> of obs =     901
> -------------+------------------------------           F( 11, 
>   889) =   17.42
>        Model |  58.4561519    11  5.31419563           Prob > 
> F      =  0.0000
>     Residual |  271.136523   889  .304990465           
> R-squared     =  0.1774
> -------------+------------------------------           Adj 
> R-squared =  0.1672
>        Total |  329.592675   900  .366214084           Root 
> MSE      =  .55226
> 
> --------------------------------------------------------------
> ----------------
> D.           |
> x |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
> -------------+------------------------------------------------
> ----------
> -------------+------
>     ln_bzo |
>          LD. |  -.0553261   .0661387    -0.84   0.403    
> -.1851322    .0744801
>              |
>  ln_tunder15 |
>          D1. |     14.637   2.988974     4.90   0.000     
> 8.770729    20.50326
>              |
>   ln_t15to24 |
>          D1. |   12.06724   2.274707     5.30   0.000     
> 7.602818    16.53166
>              |
>   ln_t25to44 |
>          D1. |   28.67612   3.411086     8.41   0.000      
> 21.9814    35.37084
>              |
>   ln_t45to64 |
>          D1. |   9.750316   3.753647     2.60   0.010     
> 2.383274    17.11736
>              |
>   ln_t65plus |
>          D1. |   9.117776   2.449422     3.72   0.000     
> 4.310454     13.9251
>              |
> ln_fractio~e |
>          D1. |   20.81802   9.297503     2.24   0.025     
> 2.570409    39.06564
>              |
> ln_pop_his~l |
>          D1. |  -3.244084   1.379805    -2.35   0.019     
> -5.95214   -.5360282
>              |
> ln_pop_nh_~k |
>          D1. |  -2.669608   2.386585    -1.12   0.264    
> -7.353605     2.01439
>              |
> ln_pop_nh_~e |
>          D1. |  -18.12162   4.160963    -4.36   0.000    
> -26.28808   -9.955169
>              |
> z |
>          D1. |  -1.102999   .2832308    -3.89   0.000    
> -1.658877   -.5471196
>              |
>        _cons |   6.306457   .0505186   124.83   0.000     
> 6.207308    6.405607
> --------------------------------------------------------------
> ----------------
> 
> 
> 
> The sample size is less than half in xtivreg. What is 
> particularly odd is the fact that the 2nd stage coefficients 
> are very close and the reported observations and groups are 
> now 1926 and 1025 for xtivreg, so just one group less than 
> xtivreg. Is it perhaps some reporting issue? I really don't 
> understand this. Just so I'm clear, the large difference, 
> especially in the coefficient of the instrument is in the 
> first stage, while the second stages are very similar.

This is curious.  I am just about to travel but I will look into it.

One thing that comes to mind is that xtivreg2 with FDs may be reporting
N=the entire sample including the singletons (group size=1) that drop
out.

Perhaps try running -xtivreg,fd- and then -xtivreg2,fd if e(sample)- so
that they use the same sample.  Do you get the same results?

> 3)
> 
> >This is confusing.  Do you mean that w_it and q_it are 
> correlated with 
> >x_it?  That's not a problem.  The key requirement is that in
> >
> >y_it=b_0+b_1 x_it + b_k demographic_covariates + w_it + q_it + e_it ,
> >
> >w_it and q_it should be uncorrelated with e_it. 
> 
> Yes, w_it and q_it are correlated with x_it and with e_it.

To repeat, correlation with x_it is not a problem, but correlation with
e_it is.

> >If they are correlated,

with e_it (sorry)

> then you have two options: (1) Add w 
> and q to 
> >your list of endogenous variables.  But as you say, you will need 
> >instruments for them.  And if you aren't interested in a causal 
> >interpretation, then maybe you shouldn't bother.  (2)  
> Instead of using 
> >w and q as regressors and instrumenting them, insert the 
> instruments as (exogenous) regressors.
> 
> I am not interested in instrumenting, just conditioning, but 
> my problem is that once I add them the previously very high 
> F-stat in the first stage goes down to the point of 
> indicating weak instruments.

It sounds like that's because the instrumenting of either w or q is
weak.  But that's what the Angrist-Pischke F-stats are for.  If the AP
F-stat for the regressor of interest is respectable, then you're OK.

> That is basically my concern. 
> Concerning your second advice, do you mean that I should just 
> drop w and q from the model and replace them with instruments?

Yes, exactly.  You'll be estimating a semi-reduced form.

--Mark
 
> Thanks again for your help, it is very much appreciated.
> 
> Best,
> 
> Filippos
> 
> >>> "Schaffer, Mark E" <[email protected]> 06/11/12 7:02 AM >>>
> Filippos,
> 
> You need to tell us more - what versions of software you are 
> using, what the actual output is (or the relevant pieces of 
> the output), etc.
> 
> More comments below.
> 
> > -----Original Message-----
> > From: [email protected]
> > [mailto:[email protected]] On Behalf Of Filippos 
> > Petroulakis
> > Sent: Sunday, June 10, 2012 11:59 PM
> > To: [email protected]
> > Subject: st: First stage of panel IV
> > 
> > Hi all,
> > 
> > I am running a panel first differences (fixed effects) model. 
> > My regression is of the sort
> > 
> > y_it=b_0+b_1 x_it + b_k demographic_covariates + e_it
> > 
> > x_it is endogenous so I have an instrument z_it.
> > 
> > I essentially have 3 issues and I list them in descending order of 
> > importance.
> > 
> > 1) xtivreg2 is running the first stage of x_it on z_it and the 
> > exogenous demographic covariates as OLS instead of fixed effects or 
> > first differences.
> 
> I doubt it very much (having programmed -xtivreg2-, I think 
> I'm well placed to say this!).  -xtivreg2- follows the 
> standard procedure of transforming the full set of variables 
> used in the same say, i.e., the within or between 
> transformation is applied to all variables.
> 
> > I honestly do not know whether
> > this is due to theory but it seems to be very odd, especially given 
> > the fixed effects is definitely the correct specification for the 
> > model as a whole, and so I would think it has to be the 
> case for the 
> > first stage as well. I can do the 2 stages manually and correct the 
> > errors using the process outlined here
> > (http://www.stata.com/support/faqs/stat/ivreg.html) and I 
> presume the 
> > fact that I have a panel doesn't change much, but my issue is 
> > basically whether this is the correct thing to do.
> > 
> > 2) xtivreg and xtivreg2 give me pretty different results, 
> which is due 
> > to the fact that xtivreg drops about half of the 
> observations in the 
> > first stage. I checked and the variable that is causing the 
> dropping 
> > (for whatever reason) is the dependent variable. I am thus positive 
> > that xtivreg is the wrong one but am still worried. Anyone 
> knows why 
> > this happens?
> 
> Again, I doubt the problem is the one you suspect.  My guess 
> is that Most likely you are using different estimators, e.g., 
> fixed effects with
> -xtivreg2- and random effects with -xtivreg-.  But you need 
> to show us the output.
> 
> > 3) Finally, at some point I will need to include a further two 
> > variables, call them w_it and q_it, which are surely endogenous. I 
> > don't care about instrumenting for them as I am not interested in a 
> > causal interpretation, but the problem is that they are also 
> > endogenous to x_it. So my first stage will be regression x_it on 
> > variables that are endogenous to itself and to y_it. Is 
> that an issue 
> > I should be concerned about?
> 
> This is confusing.  Do you mean that w_it and q_it are 
> correlated with x_it?  That's not a problem.  The key 
> requirement is that in
> 
> y_it=b_0+b_1 x_it + b_k demographic_covariates + w_it + q_it + e_it ,
> 
> w_it and q_it should be uncorrelated with e_it.  If they are 
> correlated, then you have two options: (1) Add w and q to 
> your list of endogenous variables.  But as you say, you will 
> need instruments for them.  And if you aren't interested in a 
> causal interpretation, then maybe you shouldn't bother.  (2)  
> Instead of using w and q as regressors and instrumenting 
> them, insert the instruments as (exogenous) regressors.
> 
> HTH,
> Mark
> 
> > Thank you very much in advance - answers to any or all of 
> those issues 
> > will be immensely appreciated.
> > 
> > Best,
> > 
> > Filippos Petroulakis
> > 
> > *
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> > 
> 
> 
> --
> Heriot-Watt University is the Sunday Times Scottish 
> University of the Year 2011-2012
> 
> Heriot-Watt University is a Scottish charity registered under 
> charity number SC000278.
> 
> 
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-- 
Heriot-Watt University is the Sunday Times
Scottish University of the Year 2011-2012

Heriot-Watt University is a Scottish charity
registered under charity number SC000278.


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