Boosting：为什么可以使用HP过滤器

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

摘要翻译：
霍德里克-普雷斯科特(HP)滤波器是应用宏观经济研究中应用最广泛的计量方法之一。像所有非参数方法一样，HP滤波器在很大程度上依赖于控制平滑程度的调谐参数。然而，与现代非参数方法和这些程序的应用工作相比，使用HP滤波器的经验实践几乎普遍依赖于调整参数的标准设置，这些标准设置主要是通过宏观经济数据和启发式推理的实验提出的。正如最近的研究（Phillips and Jin,2015）所表明的，标准设置可能不足以消除经济数据中的趋势，尤其是随机趋势。本文提出了一种易于实现的HP平滑器迭代的实用程序，旨在使该滤波器成为一种更智能的趋势估计和趋势消除平滑器。我们称这种迭代HP技术为boosted HP滤波器，因为它与机器学习中的$L_{2}$-Boosting有联系。本文发展了极限理论，证明了bHP滤波器渐近恢复包含单位根过程、确定性多项式漂移和具有结构突变的多项式漂移的趋势机制。一个停止准则用于自动迭代HP算法，使其成为一种数据确定的方法，为经济研究中的现代数据丰富的环境做好了准备。该方法通过三个实际数据实例进行了说明，突出了简单HP滤波、数据确定升压滤波和替代自回归方法之间的差异。这些例子表明，bHP滤波器有助于分析大量异质宏观经济时间序列，这些时间序列表现出不同程度的持续性、趋势行为和波动性。
---
英文标题：
《Boosting: Why You Can Use the HP Filter》
---
作者：
Peter C.B. Phillips, Zhentao Shi
---
最新提交年份：
2020
---
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。
--
一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

---
英文摘要：
  The Hodrick-Prescott (HP) filter is one of the most widely used econometric methods in applied macroeconomic research. Like all nonparametric methods, the HP filter depends critically on a tuning parameter that controls the degree of smoothing. Yet in contrast to modern nonparametric methods and applied work with these procedures, empirical practice with the HP filter almost universally relies on standard settings for the tuning parameter that have been suggested largely by experimentation with macroeconomic data and heuristic reasoning. As recent research (Phillips and Jin, 2015) has shown, standard settings may not be adequate in removing trends, particularly stochastic trends, in economic data.   This paper proposes an easy-to-implement practical procedure of iterating the HP smoother that is intended to make the filter a smarter smoothing device for trend estimation and trend elimination. We call this iterated HP technique the boosted HP filter in view of its connection to $L_{2}$-boosting in machine learning. The paper develops limit theory to show that the boosted HP (bHP) filter asymptotically recovers trend mechanisms that involve unit root processes, deterministic polynomial drifts, and polynomial drifts with structural breaks. A stopping criterion is used to automate the iterative HP algorithm, making it a data-determined method that is ready for modern data-rich environments in economic research. The methodology is illustrated using three real data examples that highlight the differences between simple HP filtering, the data-determined boosted filter, and an alternative autoregressive approach. These examples show that the bHP filter is helpful in analyzing a large collection of heterogeneous macroeconomic time series that manifest various degrees of persistence, trend behavior, and volatility.
---
PDF链接：
https://arxiv.org/pdf/1905.00175

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：Boosting boost ING STI Tin trend applied 宏观经济 依赖于 filter