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From |
"Scott Merryman" <[email protected]> |

To |
<[email protected]> |

Subject |
Re: st: Re: Problems with -hetgrot- |

Date |
Sun, 14 Mar 2004 18:14:12 -0600 |

----- Original Message ----- From: "Richard Williams" <[email protected]> To: <[email protected]> Sent: Sunday, March 14, 2004 5:41 PM Subject: Re: st: Re: Problems with -hetgrot- > At 04:13 PM 3/14/2004 -0600, Scott Merryman wrote: > >No doubt there are more efficient ways to do this, but here is an > >implementation > >that seems to work. > > I created my own variation called -hetgrot2-. The good news is that both > my -hetgrot2 and Scott's -gwhet2- seem to give identical results. The bad > news is that neither exactly matches the results reported by Greene! But > they come very close, and I'm not 100% sure Greene is doing it right. > > Greene, 4th edition, table 15.1, p. 598 presents an example (and the data > are included on a CD with the book). Estimates are presented for OLS, > FGLS, and ML. I found that the following Stata commands perfectly > reproduce the parameter estimates he reports. The LR statistic for > groupwise heteroscedasticity he reports is 120.915, which he says is based > on the ML statistics. I'm editing the output to just get the most crucial > parts. > > . use "W:\Greene 4th edition\DATAFILE\STATA\TBL15_1.DTA", clear > . tsset firm year > panel variable: firm, 1 to 5 > time variable: year, 1935 to 1954 > . * Model 1: Least Squares > . quietly xtgls i f c > . gwhet2 > > chi2 (4) = 104.41 > > . * Model 2: FGLS > . quietly xtgls i f c, p(h) > . gwhet2 > > chi2 (4) = 114.31 > > . * Model 3: ML > . quietly xtgls i f c, p(h) igls > . gwhet2 > > chi2 (4) = 122.89 > > The final LR statistic of 122.89 is close but not quite to Greene's > reported statistic of 120.915. I believe the discrepancy is due to the > fact that Greene uses the estimate of sigma from Model 1 (Least Squares) > while also using the estimates of the group sigmas from Model 3 (ML). I'm > not sure I understand the logic of this, but his Limdep program apparently > does it this way too. > > So the moral is, if you have a mad desire to use this test and you are > using xtgls then (1) don't use the original hetgrot, as it does it wrong, > and (2) you seem to come closest to Greene if you include the -p(h)- and > -igls- parameters on the -xtgls- command, and (3) if you wanted to do > things exactly like Greene, I think you would need a program that estimated > both models 1 and 3. From 1 you would save the OLS estimate of sigma, and > from 3 you would save the group sigmas, and then compute the test > statistic. (But again, I am not sure Greene is doing it right; at the very > least, his formulas seems internally inconsistent.) > > If your life really depends on getting these statistics right, you also may > just want to use Limdep, as these tests and the others Greene presents are > easily implemented with Limdep's TSCS (Time Series/Cross Section)command. > > Richard, I believe there is an error in Greene's text. As you reported, Greene gives the LR statistics as 120.915. However, if you compute this by hand, given the individual sigma^2, the result is: . disp 100*ln(15708.84) - 20*(ln(9410.91) + ln(755.85) + ln(34288.49) + ln(633.42) + ln(33455.51)) 104.41512 which is what you and I got. I did check the "Edition 4 Errata" (http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm) and this is not listed, though it does state that for the equation on 599 the two occurrences of "log" should be "ln." Scott * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Re: Problems with -hetgrot-***From:*Richard Williams <[email protected]>

**References**:**st: updates to -egenmore- on SSC***From:*"Nick Cox" <[email protected]>

**st: Problems with -hetgrot-***From:*"Clive Nicholas" <[email protected]>

**Re: st: Re: Problems with -hetgrot-***From:*Richard Williams <[email protected]>

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