Estimating Fixed Effects Logit Models with Large Panel Data
Estimating Fixed Effects Logit Models with Large Panel Data
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发表时间：
2016
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通讯作者：
Amrei Stammann;Florian Heiss;D. McFadden
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作者：
Amrei Stammann;Florian Heiss;D. McFadden
对于具有个体时不变效应的Logit模型的参数估计，存在条件和无条件固定效应极大似然估计。条件固定效应Logit(CL)估计是一致的，但它的缺点是它不能提供固定效应或边际效应的估计。如果每个单独T的观测值的数量很大，则计算成本也很高。无条件固定效应Logit估计量(UCL)可以通过包括每个个体的虚拟变量(DVL)来估计。另一个问题是，当个体数N很大时，DVL估计器的计算成本很高。我们在Greene(2004)和Chamberlain(1980)的精神中提出了一种伪降阶算法，其结果与DVL估计量相同，但对于大N没有计算负担。我们还讨论了如何校正附带参数、参数偏差和边际效应。蒙特卡罗证据表明，在参数估计方面，偏差修正估计与CL估计具有相似的性质。它的计算量远低于CL或DVL估计器，特别是在N和/或T较大的情况下。
For the parametric estimation of logit models with individual time-invariant effects the conditional and unconditional fixed effects maximum likelihood estimators exist. The conditional fixed effects logit (CL) estimator is consistent but it has the drawback that it does not deliver estimates of the fixed effects or marginal effects. It is also computationally costly if the number of observations per individual T is large. The unconditional fixed effects logit estimator (UCL) can be estimated by including a dummy variable for each individual (DVL). It suffers from the incidental parameters problem which causes severe biases for small T. Another problem is that with a large number of individuals N, the computational costs of the DVL estimator can be prohibitive. We suggest a pseudo-demeaning algorithm in spirit of Greene (2004) and Chamberlain (1980) that delivers the identical results as the DVL estimator without its computational burden for large N. We also discuss how to correct for the incidental parameters bias of parameters and marginal effects. Monte-Carlo evidence suggests that the bias-corrected estimator has similar properties as the CL estimator in terms of parameter estimation. Its computational burden is much lower than the CL or the DVL estimators, especially with large N and/or T.