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Linear dynamic panel-data estimation using maximum likelihood and structural equation modeling

机译：使用最大似然和结构方程建模的线性动态面板数据估计

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摘要

Panel data make it possible both to control for unobserved confounders and to include lagged, endogenous regressors. However, trying to do both simultaneously leads to serious estimation difficulties. In the econometric literature, these problems have been addressed by using lagged instrumental variables together with the generalized method of moments, while in sociology the same problems have been dealt with using maximum likelihood estimation and structural equation modeling. While both approaches have merit, we show that the maximum likelihood structural equation models method is substantially more efficient than the generalized method of moments method when the normality assumption is met and that the former also suffers less from finite sample biases. We introduce the command xtdpdml, which has syntax similar to other Stata commands for linear dynamic panel-data estimation. xtdpdml greatly simplifies the structural equation model specification process; makes it possible to test and relax many of the constraints that are typically embodied in dynamic panel models; allows one to include time-invariant variables in the model, unlike most related methods; and takes advantage of Stata's ability to use full-information maximum likelihood for dealing with missing data. The strengths and advantages of xtdpdml are illustrated via examples from both economics and sociology.
机译：面板数据既可以控制未观察到的混杂因素，又可以包含滞后的内生回归因子。但是，尝试同时执行这两个操作会导致严重的估计困难。在计量经济学文献中，这些问题已通过使用滞后的工具变量和广义矩方法来解决，而在社会学中，相同的问题已通过使用最大似然估计和结构方程模型来解决。虽然这两种方法都有其优点，但我们证明，当满足正态性假设时，最大似然结构方程模型方法比广义矩量法更有效，并且前者受有限样本偏差的影响也较小。我们引入了命令xtdpdml，该命令的语法类似于其他用于线性动态面板数据估计的Stata命令。 xtdpdml大大简化了结构方程模型的规范过程；使测试和放宽通常在动态面板模型中体现的许多约束成为可能；与大多数相关方法不同，它允许在模型中包含时不变变量；并充分利用了Stata利用完整信息的最大可能性处理丢失数据的能力。 xtdpdml的优势和优势通过经济学和社会学方面的例子得以说明。

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