发帖
雷达卡
摘要翻译：
在经济和金融的许多时间序列中，从缓慢衰减的自相关意义上说，长记忆是一个程式化的事实。分数积分过程是分析这些时间序列的主要模型。然而，关于它对预测的有用性以及如何根据它进行预测，文献中有混合的证据。通过对通货膨胀和已实现波动率时间序列的伪样本外预测和仿真，我们发现基于分数阶积分的方法明显优于不考虑长记忆的自回归和指数平滑方法。我们的建议是先验地选择一个固定的分数阶积分参数$d=0.5$，总体上得到了最好的结果，捕获了长记忆行为，但克服了使用估计参数的方法的不足。对于基于分数阶积分的预测方法的实现，我们通过仿真比较了Whittle族长记忆参数的局部和全局半参数和参数估计，并提供了仿真支持的渐近理论来比较不同的均值估计。这两项分析都导致了新的结果，这些结果在预测领域之外也很有趣。
---
英文标题：
《Forecasting under Long Memory and Nonstationarity》
---
作者：
Uwe Hassler and Marc-Oliver Pohle
---
最新提交年份：
2019
---
分类信息：
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。
--
---
英文摘要：
Long memory in the sense of slowly decaying autocorrelations is a stylized fact in many time series from economics and finance. The fractionally integrated process is the workhorse model for the analysis of these time series. Nevertheless, there is mixed evidence in the literature concerning its usefulness for forecasting and how forecasting based on it should be implemented. Employing pseudo-out-of-sample forecasting on inflation and realized volatility time series and simulations we show that methods based on fractional integration clearly are superior to alternative methods not accounting for long memory, including autoregressions and exponential smoothing. Our proposal of choosing a fixed fractional integration parameter of $d=0.5$ a priori yields the best results overall, capturing long memory behavior, but overcoming the deficiencies of methods using an estimated parameter. Regarding the implementation of forecasting methods based on fractional integration, we use simulations to compare local and global semiparametric and parametric estimators of the long memory parameter from the Whittle family and provide asymptotic theory backed up by simulations to compare different mean estimators. Both of these analyses lead to new results, which are also of interest outside the realm of forecasting.
---
PDF链接：
https://arxiv.org/pdf/1910.08202
京公网安备 11010802022788号
