On-site samples' regression : Problems of non-negative integers, truncation, and endogenous stratification
On-site samples' regression : Problems of non-negative integers, truncation, and endogenous stratification
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现场样本回归：非负整数、截断和内生分层问题
DOI：
10.1016/0304-4076(88)90003-6
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发表时间：
1988
期刊：
影响因子：
--
通讯作者：
D. Shaw
中科院分区：
文献类型：
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作者：
D. Shaw
本文纠正了一个估计问题，尚未被确认在以前的估计需求函数使用现场样本。现场样本面临三种问题，即非负整数、截断和内生分层。两个理论上正确的最大似然方法开发基于两个不同的假设变量分布：正态分布和泊松分布。使用已知模型的生成数据集进行模拟以比较这两种方法。我们不应该使用OLS，而是应该使用这里开发的最大似然方法来估计使用现场样本的需求函数。如果预测是估计的目的，那么模拟表明Poisson ML方法可能更好。
The paper corrects an estimation problem that has not yet been recognized in previous estimates of demand functions using on-site samples. There are three kinds of problems that one faces in on-site samples, namely, non-negative integers, truncation and endogenous stratification. Two theoretically correct maximum likelihood methods are developed based on two different assumptions about the variable distribution: the normal distribution and the Poisson distribution. A simulation is performed to compare the two methods using generated data sets of known models. We should not use OLS and instead should use the maximum likelihood methods developed here to estimate demand functions that use on-site samples. If forecasting is the purpose of estimation, then the simulation indicates that the Poisson ML method may be better.