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From |
ghislain dutheil <[email protected]> |

To |
[email protected] |

Subject |
Re: st: fixed effect, autocorrelation heteroskedasticity |

Date |
Mon, 25 Aug 2008 15:59:56 +0200 |

Thanks David, thanks Clive, I give more information : My data : units are countries, my T runs from 1955 to 2006 (52 years), data is balanced, response variable is normally distributed. Here my results : . xtgls y x1 x2 new_* if obs>1954,p(h) corr(ar1) note: new_pays_14 dropped due to collinearity Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: heteroskedastic Correlation: common AR(1) coefficient for all panels (0.9210) Estimated covariances = 14 Number of obs = 728 Estimated autocorrelations = 1 Number of groups = 14 Estimated coefficients = 16 Time periods = 52 Wald chi2(15) = 4919.00 Log likelihood = 952.8368 Prob > chi2 = 0.0000 y Coef. Std. Err. z P>z [95% Conf. Interval] x1 .3798278 .0294945 12.88 0.000 .3220197 .4376359 x2 .1726235 .0468473 3.68 *0.000 * .0808044 .2644425 new_pays_1 -3.460705 .1801701 -19.21 0.000 -3.813832 -3.107579 new_pays_2 -2.841054 .1648916 -17.23 0.000 -3.164236 -2.517872 new_pays_3 -3.757751 .1864565 -20.15 0.000 -4.123199 -3.392303 new_pays_4 -1.881691 .1400781 -13.43 0.000 -2.156239 -1.607143 new_pays_5 -2.138633 .1837457 -11.64 0.000 -2.498768 -1.778498 new_pays_6 -3.408386 .2389035 -14.27 0.000 -3.876628 -2.940143 new_pays_7 -2.590972 .1518792 -17.06 0.000 -2.88865 -2.293294 new_pays_8 -6.032898 .3024923 -19.94 0.000 -6.625772 -5.440024 new_pays_9 -3.028034 .1719979 -17.61 0.000 -3.365143 -2.690924 new_pays_10 -3.71647 .197751 -18.79 0.000 -4.104055 -3.328885 new_pays_11 -3.792297 .2487666 -15.24 0.000 -4.279871 -3.304724 new_pays_12 -3.284582 .2416106 -13.59 0.000 -3.75813 -2.811034 new_pays_13 -1.64241 .1429002 -11.49 0.000 -1.922489 -1.36233 _cons 4.764067 .5989794 7.95 0.000 3.590089 5.938045 xtpcse y x1 x2 new_* if obs>1954, correlation(ar1) (note: estimates of rho outside [-1,1] bounded to be in the range [-1,1]) Prais-Winsten regression, correlated panels corrected standard errors (PCSEs) Group variable: newid Number of obs = 728 Time variable: obs Number of groups = 14 Panels: correlated (balanced) Obs per group: min = 52 Autocorrelation: common AR(1) avg = 52 max = 52 Estimated covariances = 105 R-squared = 0.9620 Estimated autocorrelations = 1 Wald chi2(15) = 5937.36 Estimated coefficients = 16 Prob > chi2 = 0.0000 Panel-corrected Coef. Std. Err. z P>z [95% Conf. Interval] x1 .4503796 .0454639 9.91 0.000 .361272 .5394873 x2 .122018 .0775376 1.57 *0.116 * -.029953 .273989 new_pays_1 2.368664 .2301929 10.29 0.000 1.917494 2.819833 new_pays_2 2.913281 .2588473 11.25 0.000 2.40595 3.420613 new_pays_3 2.095312 .2180982 9.61 0.000 1.667848 2.522777 new_pays_4 3.825269 .2940457 13.01 0.000 3.24895 4.401587 new_pays_5 3.552052 .3126555 11.36 0.000 2.939259 4.164846 new_pays_6 2.481951 .2716012 9.14 0.000 1.949623 3.01428 new_pays_7 3.141937 .2687181 11.69 0.000 2.615259 3.668614 new_pays_8 (dropped) new_pays_9 2.771673 .2540433 10.91 0.000 2.273758 3.269589 new_pays_10 2.157031 .2128672 10.13 0.000 1.739819 2.574243 new_pays_11 2.112108 .2568422 8.22 0.000 1.608707 2.615509 new_pays_12 2.572271 .2679651 9.60 0.000 2.047069 3.097473 new_pays_13 4.04784 .2898361 13.97 0.000 3.479771 4.615908 new_pays_14 5.499673 .4054914 13.56 0.000 4.704925 6.294422 _cons -1.236201 .9730438 -1.27 0.204 -3.143331 .6709301 rho .9190896 As you see X1 is significant in the two case, but X2 is not. My theoretical economical question is X2 is or is not explicative of Y ? So if i take one model i have one answer if i take the other i have an other one, i have to choose very carrefully. Clive said " Much depends on how much contemporaneous correlation of the errors there is in your data. If you have lots, and T > N by a factor of 3 or more (which you have), then FGLS estimates should be okay. If you don't have much by way of CCEs, then OLS-PCSE is to be preferred" I don't how to know if there is a lots or a few CCE ? Can you help me Thanks Ghislain Clive Nicholas a �crit : > Ghislain Dutheil replied: > > >> the two model give results quite different : in one case (FGLS) an >> explonary variable is significative in the other, PCSE, it is not, and i >> have only two explonary variables... so the difference is sensible . So >> excuse me but why cross-sectional GLS estimates is pretty unreliable >> compare to OLS-PCSE ? >> > > Since you neither show us any output from your models nor explain what > these models seek to explain theoretically, there really is no way of > judging how 'sensible' your results are. Only you will know. > > Much depends on how much contemporaneous correlation of the errors > there is in your data. If you have lots, and T > N by a factor of 3 or > more (which you have), then FGLS estimates should be okay. If you > don't have much by way of CCEs, then OLS-PCSE is to be preferred; see > the whole of Beck and Katz (1995). In most panel data (T > 50 is not > typical), the paramter estimates are inefficient under FGLS: "The FGLS > standard errors underestimate sampling variability because FGLS > assumes that \sigma [the N x N matrix of contemporaneous covariances] > is known, not estimated. Our conclusion is that the Parks-Kmenta > [FGLS] estimator simply should not be used" (Beck, 2001). > > However, you _still_ haven't really told us about your data. We're > still left to assume that your units are countries (which would rule > out -bootstrap-ping or -simulate-ing your way out of any > difficulties), that your Ts are equidistantly spaced, and that your > response variable is normally distributed. If your RV isn't, then none > of the modelling approaches mentioned in this thread may be useful for > fitting to your data. > > * * 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/

**Follow-Ups**:**Re: st: fixed effect, autocorrelation heteroskedasticity***From:*"Clive Nicholas" <[email protected]>

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