摘要翻译：
本文讨论了一个小问题：如果我们用一个二元因变量对数据进行分组，并希望在规范中包含固定效应（分组特定截取），那么普通最小二乘(OLS)是否比（条件）logit形式更好？特别是，使用OLS代替固定效应logit模型，对于后者删除因变量中没有变化的所有单位，而前者允许使用所有单位进行估计，会有什么后果。首先，我们证明了对随机参数问题的讨论是建立在对被研究数据种类的假设基础上的；对于固定效应模型在政治学中的普遍使用而言，附带参数问题是虚幻的。转到线性模型，我们看到OLS产生了因变量有变化和无变化的单位估计的线性组合，因此必须仔细解释系数估计。文章接着比较了两种估计具有固定效应的logit模型的方法，并表明Chamberlain条件logit与简单地包括特定群截取的logit分析一样好或更好（尽管条件logit技术是为了处理附带参数问题而设计的！）。与此相关，本文讨论了利用OLS和Logit估计边际效应的方法。虽然可以用固定效应的logit形式来估计边际效应，但这种方法可以从条件logit开始，然后用这些参数估计来约束固定效应logit模型。这种方法产生的样本平均边际效应估计至少与OLS一样好，而且当组规模较小或组数量较多时更好。.
---
英文标题：
《Estimating grouped data models with a binary dependent variable and
  fixed effects: What are the issues》
---
作者：
Nathaniel Beck
---
最新提交年份：
2018
---
分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--

---
英文摘要：
  This article deals with asimple issue: if we have grouped data with a binary dependent variable and want to include fixed effects (group specific intercepts) in the specification, is Ordinary Least Squares (OLS) in any way superior to a (conditional) logit form? In particular, what are the consequences of using OLS instead of a fixed effects logit model with respect to the latter dropping all units which show no variability in the dependent variable while the former allows for estimation using all units. First, we show that the discussion of fthe incidental parameters problem is based on an assumption about the kinds of data being studied; for what appears to be the common use of fixed effect models in political science the incidental parameters issue is illusory. Turning to linear models, we see that OLS yields a linear combination of the estimates for the units with and without variation in the dependent variable, and so the coefficient estimates must be carefully interpreted. The article then compares two methods of estimating logit models with fixed effects, and shows that the Chamberlain conditional logit is as good as or better than a logit analysis which simply includes group specific intercepts (even though the conditional logit technique was designed to deal with the incidental parameters problem!). Related to this, the article discusses the estimation of marginal effects using both OLS and logit. While it appears that a form of logit with fixed effects can be used to estimate marginal effects, this method can be improved by starting with conditional logit and then using the those parameter estimates to constrain the logit with fixed effects model. This method produces estimates of sample average marginal effects that are at least as good as OLS, and much better when group size is small or the number of groups is large. .
---
PDF链接：
https://arxiv.org/pdf/1809.06505