Specification Choices in Quantile Regression for Empirical Macroeconomics
研究了宏观应用中分位数回归的设定选择，包括是否及如何加入收缩方法，以及使用经典还是贝叶斯框架。通过预测准确性比较，发现收缩通常有助于提高分位数预测精度，且贝叶斯方法优于频率学派方法。
ABSTRACT Quantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks. This paper examines various choices in the specification of quantile regressions for macro applications, including how and to what extent to include shrinkage and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, measured with quantile scores and quantile‐weighted continuous ranked probability scores at a range of quantiles from the left to right tail. Across applications, we find that shrinkage is generally helpful to quantile forecast accuracy, with Bayesian quantile regression dominating frequentist quantile regression.