Dynamic models for dynamic theories: The ins and outs of lagged dependent variables
Dynamic models for dynamic theories: The ins and outs of lagged dependent variables
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DOI：
10.1093/pan/mpj006
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发表时间：
2006-03-01
影响因子：
5.4
通讯作者：
Kelly, NJ
中科院分区：
文献类型：
--
作者：
Keele, L;Kelly, NJ
OLS回归中的滞后因变量通常被用作捕捉政治过程中动态效应的手段，并作为消除模型自相关的方法。但最近的研究认为，滞后的因变量规范在大多数情况下使用问题太大。更具体地说，如果存在残差自相关，滞后的因变量会导致解释变量的系数向下偏倚。我们使用蒙特卡罗分析来经验地评估在各种情况下使用滞后因变量时存在多少偏差。在我们的分析中，我们将滞后因变量模型的性能与其他几个时间序列模型进行了比较。我们表明，虽然滞后因变量在某些情况下是不合适的，但它仍然是应用分析人员经常测试的动态理论的适当模型。从分析中，我们提出了一些关于何时以及如何在模型右侧使用滞后因变量的实用建议。
A lagged dependent variable in an OLS regression is often used as a means of capturing dynamic effects in political processes and as a method for ridding the model of autocorrelation. But recent work contends that the lagged dependent variable specification is too problematic for use in most situations. More specifically, if residual autocorrelation is present, the lagged dependent variable causes the coefficients for explanatory variables to be biased downward. We use a Monte Carlo analysis to assess empirically how much bias is present when a lagged dependent variable is used under a wide variety of circumstances. In our analysis, we compare the performance of the lagged dependent variable model to several other time series models. We show that while the lagged dependent variable is inappropriate in some circumstances, it remains an appropriate model for the dynamic theories often tested by applied analysts. From the analysis, we develop several practical suggestions on when and how to use lagged dependent variables on the right-hand side of a model.