论文标题
关于本地投影模型的可能性
On the Likelihood of Local Projection Models
论文作者
论文摘要
局部投影模型由一组线性回归定义，这些线性回归解释了外源变量与在不同时间点上观察到的内源变量之间的关联。尽管使用普通最小二乘估计器分别估计单个回归是标准做法，但一些最近的研究将局部投影模型视为具有相关误差的多元回归，即看似无关的回归，并提出了贝叶斯和非划线方法，以提高估计准确性。但是，尚不清楚这种治疗当地投射模型的方式以及何时是合理的。本文的主要目的是通过表明可以分析局部投影模型的可能性来填补这一空白。通过数值实验，我们确认对局部预测的这种处理对于有限样品是可替代的。
A local projection model is defined by a set of linear regressions that account for the associations between exogenous variables and an endogenous variable observed at different time points. While it is standard practice to separately estimate individual regressions using the ordinary least squares estimator, some recent studies treat a local projection model as a multivariate regression with correlated errors, i.e., seemingly unrelated regressions, and propose Bayesian and non-Bayesian methods to improve the estimation accuracy. However, it is not clear how and when this way of treatment of local projection models is justified. The primary purpose of this paper is to fill this gap by showing that the likelihood of local projection models can be analytically derived from a stationary vector moving average process. By means of numerical experiments, we confirm that this treatment of local projections is tenable for finite samples.
