Title xtlogit — Fixed-effects, random-effects, and population-averaged logit models stata.com Description Syntax Options for PA model Methods and formulas Quick start Options for RE model Remarks and examples References Menu Options for FE model Stored results Also see Description xtlogit fits random...
Log likelihood = -7436.9862 Conditional fixed-effects logistic regression Number of obs = 71,017 Group variable: newid Number of groups = 14,737 Obs per group: min = 2 avg = 4.8 max = 10 LR chi2(18) = 35362.49 Log likelihood = -7436.9862 Prob > chi2 = 0.0000 --- leadgetfun~d |...
I had intended to compare fixed and random effects models. Questions: I would have assumed that after setting the panel the fixed effects estimators using model based standard errors would have adjusted for clustering within id. I assumed they would be consistent but perhaps not efficient. Am I...
fe use fixed-effects estimator offset(varname) include varname in model with coefficient constrained to 1 constraints(constraints) apply specified linear constraints collinear keep collinear variables SE vce(vcetype) vcetype may be oim, bootstrap, or jackknife...
移动平均法是用一组最近的实际数据值来预测未来一期或几期内公司产品的需求量、公司产能等的一种常用...
With the Logit including fixed-effects: ---xstatus | Coef. Std. Err. z P>|z| [95% Conf.Interval]---+---lgdpc | 1.176038 .178538 6.59 0.000 .82610991.525966lpop | .9565665 .0688508 13.89 0.000.8216214 1.091512ldist | -.9724132 .093985 -10.35 0.000-1.15662 -.7882059 ...
Dear all I want to run an xtlogit with fixed effects but Stata won't do it. It complains about multiple positive outcomes within groups. I did exclude observations where all outcomes are 1 or 0. It then reports a sorting error. Why? Dirk Nachbar...
Conditional fixed-effects logistic regression Number of obs =360Group variable: pays Number of groups =10Obs per group: min =36avg =36.0max =36LR chi2(9) =122.33Log likelihood = -126.87524 Prob > chi2 =0.0000 --- conflit | Coef. Std. Err. z P>|z| [95% Conf.Interval] ---+--...
correlation between the fixed effects (physician covariates) and the random effect, then the parameters are liable not to be consistently estimated. But when stuck with a small data set, why not run a model designed for that data structure, as opposed to running a model not designed for the...
(), is required, so there is no need for the robust option. That is my understanding. Hope this helps. Sam On Tue, 23 Jul 2002, Anderson, Soren wrote: > Hi. I'm looking for a quick (footnote) explanation for why there is no "robust" option for the fixed effects (conditional) ...