| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w26750 |
| 来源ID | Working Paper 26750 |
| An Exploration of Trend-Cycle Decomposition Methodologies in Simulated Data | |
| Robert J. Hodrick | |
| 发表日期 | 2020-02-17 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | This paper uses simulations to explore the properties of the HP filter of Hodrick and Prescott (1997), the BK filter of Baxter and King (1999), and the H filter of Hamilton (2018) that are designed to decompose a univariate time series into trend and cyclical components. Each simulated time series approximates the natural logarithms of U.S. Real GDP, and they are a random walk, an ARIMA model, two unobserved components models, and models with slowly changing nonstationary stochastic trends and definitive cyclical components. In basic time series, the H filter dominates the HP and BK filters in more closely characterizing the underlying framework, but in more complex models, the reverse is true. |
| 主题 | Macroeconomics ; Business Cycles |
| URL | https://www.nber.org/papers/w26750 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/584424 |
| 推荐引用方式 GB/T 7714 | Robert J. Hodrick. An Exploration of Trend-Cycle Decomposition Methodologies in Simulated Data. 2020. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w26750.pdf（919KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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