Modeling the distribution of key economic indicators in a data-rich environment: new empirical evidence

This study explores the ability of a large number of macroeconomic variables to forecast the mean, quantiles and density of key economic indicators. In the baseline case, we construct the forecasts using an autoregressive model. We then consider several general specifications that augment the time series model with macroeconomic information, either directly using the full set of predictors, through targeted-factors, targeted-predictors or forecast combinations. Our findings suggest that aggregating information across quantiles leads to improved estimates of the conditional mean. Overall, augmenting the autoregressive model with macroeconomic variables through methods that perform variable selection or account for non-linearities improves predictive performance. This increase in out-of-sample performance arises from the improved estimation of the lower and middle part of the distribution.

本研究探讨了海量宏观经济变量对关键经济指标的均值、分位数与分布密度的预测能力。基准情形下，我们采用自回归模型构建预测结果。随后，我们考量了多种通用建模设定：通过直接使用全部预测变量集、靶向因子（targeted-factors）、靶向预测变量（targeted-predictors）或预测组合的方式，将宏观经济信息纳入时间序列模型以对其进行扩充。研究结果表明，跨分位数聚合信息可优化条件均值的估计效果。总体而言，通过变量选择或考虑非线性的方法，将宏观经济变量纳入自回归模型，可提升模型的预测性能。样本外性能的提升，源于对分布下限与中部区间的估计精度得到优化。