英文标题：
《Long-term prediction intervals with many covariates》
---
作者：
Sayar Karmakar, Marek Chudy and Wei Biao Wu
---
最新提交年份：
2021
---
英文摘要：
Accurate forecasting is one of the fundamental focus in the literature of econometric time-series. Often practitioners and policy makers want to predict outcomes of an entire time horizon in the future instead of just a single $k$-step ahead prediction. These series, apart from their own possible non-linear dependence, are often also influenced by many external predictors. In this paper, we construct prediction intervals of time-aggregated forecasts in a high-dimensional regression setting. Our approach is based on quantiles of residuals obtained by the popular LASSO routine. We allow for general heavy-tailed, long-memory, and nonlinear stationary error process and stochastic predictors. Through a series of systematically arranged consistency results we provide theoretical guarantees of our proposed quantile-based method in all of these scenarios. After validating our approach using simulations we also propose a novel bootstrap based method that can boost the coverage of the theoretical intervals. Finally analyzing the EPEX Spot data, we construct prediction intervals for hourly electricity prices over horizons spanning 17 weeks and contrast them to selected Bayesian and bootstrap interval forecasts.
---
中文摘要：
准确预测是计量经济时间序列文献中的一个基本焦点。通常，从业者和政策制定者希望预测未来整个时间范围的结果，而不是仅仅提前一步预测。这些序列除了自身可能的非线性依赖性外，还经常受到许多外部预测因素的影响。在本文中，我们在高维回归环境中构造时间聚合预测的预测区间。我们的方法基于由流行套索程序获得的残差分位数。我们考虑了一般的重尾、长记忆、非线性平稳误差过程和随机预测。通过一系列系统排列的一致性结果，我们为我们提出的基于分位数的方法在所有这些场景中提供了理论保证。在通过仿真验证了我们的方法之后，我们还提出了一种新的基于bootstrap的方法，可以提高理论间隔的覆盖率。最后，通过分析EPEX现货数据，我们构建了17周内每小时电价的预测区间，并将其与选定的贝叶斯和bootstrap区间预测进行了对比。
---
分类信息：

一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
--
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--
一级分类：Mathematics 数学
二级分类：Statistics Theory 统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
--
一级分类：Statistics 统计学
二级分类：Statistics Theory 统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
--

---
PDF下载：
-->