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From |
Hewan Belay <[email protected]> |

To |
Stata List <[email protected]> |

Subject |
Re: st: time-invariant regressors in xtdpdsys |

Date |
Tue, 5 Aug 2008 09:02:22 -0700 (PDT) |

--CORRECTION -- [Apologies, there was an important error in my question earlier. Below is the corrected version] I want to add to my question below a more stata-oriented (as opposed to econometric) question but related to the same topic. I estimated a dynamic model including time-invariant regressors using -xtdpdsys-. Since -xtdpd- is a generalised version of the former, it should be possible to replicate this estimation in -xtdpd-, but I was not able to. Here is an example: webuse abdata, clear by id, s: egen avg_emp = mean(emp) /* No. 1 */ xtdpdsys n w k avg /* No. 2 */ xtdpdsys n w k /* No. 3 */ xtdpd L(0/1).n w k avg, div(w k avg) dgmmiv(n) lgmmiv(n) Equation 3 was an attempt to replicate the results of equation 1. Instead however, (3) produces results identical to (2) i.e. is not able to identify the time-invariant variable. I tried different ways of writing the -xtdpd- command to get the results of (1), to no avail. How can -xtdpd- be made to replicate Blundell-Bond with time-invariant regressors, which it should be able to, since it is supposed to be able to accomodate estimations of Blundell-Bond as well as Arellano-Bond (or -xtdpdsys- and - xtabond-)? Below please find the full results of the above stated commands. Thanks in advance, Hewan .. webuse abdata, clear .. by id, s: egen avg_emp = mean(emp) .. /* No. 1 */ xtdpdsys n w k avg note: avg_emp dropped from div() because of collinearity System dynamic panel-data estimation Number of obs = 891 Group variable: id Number of groups = 140 Time variable: year Obs per group: min = 6 avg = 6.364286 max = 8 Number of instruments = 38 Wald chi2(4) = 4550.53 Prob > chi2 = 0.0000 One-step results ------------------------------------------------------------------------------ n | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- n | L1. | .5514304 .043601 12.65 0.000 .465974 .6368869 w | -.6130319 .0513926 -11.93 0.000 -.7137595 -.5123042 k | .3334923 .0239851 13.90 0.000 .2864823 .3805022 avg_emp | -.0032794 .0042283 -0.78 0.438 -.0115668 .0050079 _cons | 2.541278 .1785823 14.23 0.000 2.191263 2.891293 ------------------------------------------------------------------------------ Instruments for differenced equation GMM-type: L(2/.).n Standard: D.w D.k Instruments for level equation GMM-type: LD.n Standard: _cons .. /* No. 2 */ xtdpdsys n w k System dynamic panel-data estimation Number of obs = 891 Group variable: id Number of groups = 140 Time variable: year Obs per group: min = 6 avg = 6.364286 max = 8 Number of instruments = 38 Wald chi2(3) = 4601.24 Prob > chi2 = 0.0000 One-step results ------------------------------------------------------------------------------ n | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- n | L1. | .5419106 .0416037 13.03 0.000 .4603688 .6234524 w | -.6152988 .0510225 -12.06 0.000 -.715301 -.5152965 k | .3306255 .0235661 14.03 0.000 .2844369 .3768142 _cons | 2.531675 .1771563 14.29 0.000 2.184455 2.878895 ------------------------------------------------------------------------------ Instruments for differenced equation GMM-type: L(2/.).n Standard: D.w D.k Instruments for level equation GMM-type: LD.n Standard: _cons .. /* No. 3 */ xtdpd L(0/1).n w k avg, div(w k avg) dgmmiv(n) lgmmiv(n) note: avg_emp dropped from div() because of collinearity note: D.avg_emp dropped because of collinearity Dynamic panel-data estimation Number of obs = 891 Group variable: id Number of groups = 140 Time variable: year Obs per group: min = 6 avg = 6.364286 max = 8 Number of instruments = 38 Wald chi2(3) = 4601.24 Prob > chi2 = 0.0000 One-step results ------------------------------------------------------------------------------ n | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- n | L1. | .5419106 .0416037 13.03 0.000 .4603688 .6234524 w | -.6152988 .0510225 -12.06 0.000 -.715301 -.5152965 k | .3306255 .0235661 14.03 0.000 .2844369 .3768142 _cons | 2.531675 .1771563 14.29 0.000 2.184455 2.878895 ------------------------------------------------------------------------------ Instruments for differenced equation GMM-type: L(2/.).n Standard: D.w D.k Instruments for level equation GMM-type: LD.n Standard: _cons > > From Hewan Belay <[email protected]> > To Stata List <[email protected]> > Subject st: time-invariant regressors in xtdpdsys > Date Mon, 4 Aug 2008 18:20:53 -0700 (PDT) > > ----------------------------------------------------------------------- > Dear List, > > I have been trying to learn about the properties of the > estimates of time- > invariant regressors obtained when estimating a dynamic > panel data model > with the Blundell-Bond method, using -xtdpdsys- or -xtdpd- > , e.g. estimating the model > > y_it = a + b*y_it-1 + c*x_it + d*z_i + u_i + e_it > > so my question refers to the estimator d-hat. One of the > big attractions of using Arellano-Bover/Blundell-Bond ( > -xtdpdsys- ) > rather than Arellano-Bond ( -xtabond- ) is that parameters > of time- > invariant explanatory variables can be identified ... in > addition to the > other attractions (consistency and greater precision when T > is small, n is > small, and the true value of the parameter b (see above) is > large in > absolute value). > > But neither the stata manual's discussions of > -xtdpdsys- and -xtdpd-, nor > for that matter the paper Blundell and Bond (1998), discuss > the properties > of the estimates of time-fixed variables' parameters. > The paper only > explores an AR(1) model, i.e. the RHS contains only the LDV > plus the > errors, and then uses the usual UK data (see -webuse > abdata- ) with time- > varying regressors only. The stata manual accordingly only > picks up on the > discussion based on the UK data results. Nor have I seen > much discussion > on this in other articles. > > Any directions, or references, would be much appreciated! > Hewan * * 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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