融合LASSO作为非交叉分位数回归
Fused LASSO as Non-Crossing Quantile Regression
AI总结：
针对风险增长（Growth-at-Risk）的分位数交叉问题，提出在估计中施加非交叉约束（等价于带分位数特异性收缩参数的融合LASSO），改进左尾预测与分位数变异驱动变量识别，且该改进对相关政策工具有影响。
摘要：
风险增长（Growth-at-Risk）对实证宏观经济学至关重要，但由于数据限制，其分位数交叉问题常受质疑。现有文献通过对拟合分位数的后处理解决该问题，但这些方法不修正估计系数。我们主张在估计过程中施加非交叉约束，并证明其等价于带有分位数特异性收缩参数的融合LASSO。通过分位数间收缩视角重新审视风险增长，我们实现了改进的左尾预测，更好地识别了驱动分位数变异的变量。我们表明这些改进对预期短缺（Expected Shortfall）和分位数局部投影（Quantile Local Projections）等政策工具具有影响。
英文摘要：
Growth-at-Risk is vital for empirical macroeconomics but is often suspect to quantile crossing due to data limitations. While existing literature addresses this through post-processing of the fitted quantiles, these methods do not correct the estimated coefficients. We advocate for imposing non-crossing constraints during estimation and demonstrate their equivalence to fused LASSO with quantile-specific shrinkage parameters. By re-examining Growth-at-Risk through an interquantile shrinkage lens, we achieve improved left-tail forecasts and better identification of variables that drive quantile variation. We show that these improvements have ramifications for policy tools such as Expected Shortfall and Quantile Local Projections.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
