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From |
"Erik Melander" <erik.melander@pcr.uu.se> |

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

Subject |
RE: st: ordinal dynamic panel data |

Date |
Mon, 16 Feb 2004 22:05:41 +0200 |

> -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Stas Kolenikov > Sent: Tuesday, January 27, 2004 7:47 PM > To: statalist@hsphsun2.harvard.edu > Subject: Re: st: ordinal dynamic panel data > > > Another potential problem I see in those results is that the cuts / > thresholds give a very broad range of 5.4. It means that the underlying > index should have at least that much of a range, or better a broader > range. I would suspect that most of your data fall somewhere in the > beginning of the scale, in the first two categories or so, and the highest > categories of 4 and 5 are only observed in a handful of observations in > the tail of the normal distribution, so those are not estimated very > accurately. If that is the case, you might want to pool the upper > categories into something like 4 and above. I have now tested what happens when I pool categories 4 and 5, but very similar results are obtained. I get absurd predictions based on the models I estimate, often way off the actual range of the dependent variable (1 to 5). I suppose this is a consequence of the strangely broad range of the cuts/thresholds. I have also tried ordered probit and ordered logit using gllamm, but the results are again very similar. Is there anything that I can do to arrive at models that produce more reasonable predictions? > > What exactly are you interested in in your model? What is the question > that you need to answer with it? The estimated model will have some > statistical problems from various sides, so you may need to think more > about it before submitting it for a publication, but I personally would > utilize those results to at least assess where I stand, especially as long > as I don't see any better way to go with this model. > I am foremostly interested in demonstrating that one particular independent variable significantly affects the level of pesonal integrity rights abuse while controlling for the other determinants identified in previous research. I also want to examine interaction effects involving my new explanatory factor and one of the established predictors. In order to show that my new variable also has substantially important impact I want to compare the predicted levels of abuse for different combinations of independent variables. Thanks for your explanations and suggestions. Erik Melander > > Fitting constant-only model: > > > > Iteration 0: log likelihood = -1913.2714 > > Iteration 1: log likelihood = -1624.8395 > > rho >= 1, set to rho = 0.99 > > Iteration 2: log likelihood = -1618.5555 (not concave) > > Iteration 3: log likelihood = -1612.0323 (not concave) > > Iteration 4: log likelihood = -1610.2913 > > Iteration 5: log likelihood = -1609.5952 > > Iteration 6: log likelihood = -1609.5949 > > Iteration 7: log likelihood = -1609.5949 > > > > Fitting full model: > > > > Iteration 0: log likelihood = -1416.2615 (not concave) > > Iteration 1: log likelihood = -1378.9136 (not concave) > > Iteration 2: log likelihood = -1362.3694 > > Iteration 3: log likelihood = -1352.2499 > > Iteration 4: log likelihood = -1351.2405 > > Iteration 5: log likelihood = -1351.2266 > > Iteration 6: log likelihood = -1351.2266 > > > > Random Effects Ordered Probit Number of obs = > > 1615 > > LR chi2(12) = > > 516.74 > > Log likelihood = -1351.2266 Prob > chi2 = > > 0.0000 > > > > > ------------------------------------------------------------------ > ---------- > > -- > > PolTS | Coef. Std. Err. z P>|z| [95% Conf. > > Interval] > > > -------------+---------------------------------------------------- > ---------- > > -- > > eq1 | > > y t-1 =2 | 1.190776 .1362784 8.74 0.000 .923675 > > 1.457877 > > y t-1 =3 | 1.984349 .1622633 12.23 0.000 1.666319 > > 2.30238 > > y t-1 =4 | 2.873937 .190133 15.12 0.000 2.501283 > > 3.246591 > > y t-1 =5 | 3.719126 .2362928 15.74 0.000 3.256001 > > 4.182252 > > X1 | -.0177493 .0063743 -2.78 0.005 -.0302427 > -.0052559 > > X2 | -.0329977 .0071047 -4.64 0.000 -.0469225 > -.0190728 > > X3 | 1.229424 .6062995 2.03 0.043 .0410985 > 2.417749 > > X4 | -.1695904 .0519423 -3.26 0.001 -.2713954 > -.0677854 > > X5 | .194112 .0497613 3.90 0.000 .0965816 > 2916424 > > X6 | .9392769 .1504445 6.24 0.000 .6444111 > 1.234143 > > X7 | .6116019 .1881763 3.25 0.001 .2427832 > 9804206 > > > -------------+---------------------------------------------------- > ---------- > > -- > > _cut1 | > > _cons | 1.075184 .850905 1.26 0.206 -.5925591 > > 2.742927 > > > -------------+---------------------------------------------------- > ---------- > > -- > > _cut2 | > > _cons | 3.07571 .856076 3.59 0.000 1.397832 > > 4.753588 > > > -------------+---------------------------------------------------- > ---------- > > -- > > _cut3 | > > _cons | 4.857695 .864742 5.62 0.000 3.162832 > > 6.552558 > > > -------------+---------------------------------------------------- > ---------- > > -- > > _cut4 | > > _cons | 6.432284 .8734574 7.36 0.000 4.720339 > > 8.144229 > > > -------------+---------------------------------------------------- > ---------- > > -- > > rho | > > _cons | .2358363 .0489779 4.82 0.000 .1398414 > > .3318312 > > > ------------------------------------------------------------------ > * http://www.ats.ucla.edu/stat/stata/ * * 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/

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**st: How to compare panel data fit in STATA?***From:*"ACHINTYA RAY" <achintya.ray@vanderbilt.edu>

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