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From |
"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: RE: First stage of panel IV |

Date |
Sat, 16 Jun 2012 12:24:28 +0100 |

Hi Filippos. Sorry for the delay in replying. Some responses below: > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of > Filippos Petroulakis > Sent: 13 June 2012 00:15 > To: statalist@hsphsun2.harvard.edu > Subject: RE: st: RE: First stage of panel IV > > Hi Mark, > > 1) I have version 01.0.13 for xtivreg2, 03.0.08 for ivreg2, > and 01.3.01 for ranktest xtivreg2 is up-to-date but ivreg2 and ranktest are not. I tried to replicate your problem (different first-stage results reported by xtivreg2 and official xtivreg when there are singletons) but couldn't - using the up-to-date versions, the first-stage and final outputs of xtivreg2 and xtivreg match. If updating doesn't solve your problem, can you contact me off-list and we can try to work out what's going on? > 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). Apologies - in your previous posting you said 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 are correlated with x_it and with e_it" so I thought you were instrumenting for them. But now I understand. So ... if I understand correctly, what is happening is that the b_1, the coeff on the endogenous regressor x_it, becomes weakly identified when you include w and q as exogenous regressors. I think what is happening is that the component of x that your excluded instrument z is correlated with is also correlated with w and q. If you think about it in a mechanical way, the weak ID diagnostic is the first-stage F stat for the significance of x in the regression x_it = b_k demographic_covariates + z_it + v_it The F for a test of the significance of z in the above regression is big, but when you add w and q, x_it = b_k demographic_covariates + z_it + w_it + q_it + v_it the F for the test of z becomes small. So, loosely speaking, a lot of the ability of z to explain x disappears when it has to compete with w and q. Presumably the SEs on w and q are on the small size. I don't know what your application is, but it's possible that this could be a case of what Angrist and Pischke ("Mostly Harmless Econometrics", Princeton U.P. 2009, pp. 64-68) call the "bad control" problem. If so, the solution is to omit w and q altogether. HTH, Mark > Thanks again, > > Filippos > > >>> "Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk> 06/12/12 4:36 AM >>> > Filippos, > > > -----Original Message----- > > From: owner-statalist@hsphsun2.harvard.edu > > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Filippos > > Petroulakis > > Sent: 12 June 2012 04:11 > > To: statalist@hsphsun2.harvard.edu > > 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" <M.E.Schaffer@hw.ac.uk> 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: owner-statalist@hsphsun2.harvard.edu > > > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf > Of Filippos > > > Petroulakis > > > Sent: Sunday, June 10, 2012 11:59 PM > > > To: statalist@hsphsun2.harvard.edu > > > 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 > > > > > > * > > > * 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/ > > > > > > > > > -- > > 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. > > > > > > * > > * 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/ > > > > > > > > * > > * 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/ > > > > > -- > 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. > > > * > * 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/ > > > > * > * 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/ > -- 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. * * 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/

**References**:**RE: st: RE: First stage of panel IV***From:*"Filippos Petroulakis" <petroulakis@econ.umd.edu>

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