论文标题
通过因子增强因子的分位数自动重测的预测：模型平均方法
Forecasting With Factor-Augmented Quantile Autoregressions: A Model Averaging Approach
论文作者
论文摘要
本文考虑了在模型平均框架下对英国的增长和通货膨胀分布的预测。我们使用权重调查模型组合，以最大程度地减少Akaike信息标准（AIC），贝叶斯信息标准（BIC），分位数回归信息标准（QRIC）以及保留的交叉验证标准。未观察到的因素是通过在递归估计方案下在t期间内带有n个预测因子的大面板的主要成分来估计的。我们将上述方法应用于英国GDP增长和CPI通胀率。我们发现，对于GDP增长，在覆盖范围和最终预测误差方面，AIC和BIC所获得的同等权重或权重相同，但在大多数分位数上，AIC和BIC获得的权重表现都均优于QRIC和Jackknife方法。相反，天真的QAR（1）通货膨胀模型优于所有模型平均方法。
This paper considers forecasts of the growth and inflation distributions of the United Kingdom with factor-augmented quantile autoregressions under a model averaging framework. We investigate model combinations across models using weights that minimise the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Quantile Regression Information Criterion (QRIC) as well as the leave-one-out cross validation criterion. The unobserved factors are estimated by principal components of a large panel with N predictors over T periods under a recursive estimation scheme. We apply the aforementioned methods to the UK GDP growth and CPI inflation rate. We find that, on average, for GDP growth, in terms of coverage and final prediction error, the equal weights or the weights obtained by the AIC and BIC perform equally well but are outperformed by the QRIC and the Jackknife approach on the majority of the quantiles of interest. In contrast, the naive QAR(1) model of inflation outperforms all model averaging methodologies.
