Modeling the distribution of key economic indicators in a data-rich environment: new empirical evidence
研究利用大量宏观经济变量预测关键经济指标的均值、分位数和密度，发现通过变量选择或非线性方法增强自回归模型可提升预测性能，尤其改善分布低端和中部的估计。
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.