| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29833 |
| 来源ID | Working Paper 29833 |
| High-Dimensional Factor Models with an Application to Mutual Fund Characteristics | |
| Martin Lettau | |
| 发表日期 | 2022-03-14 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | This paper considers extensions of two-dimensional factor models to higher-dimensional data represented as tensors. I describe decompositions of tensors that generalize the standard matrix singular value decomposition and principal component analysis to higher dimensions. I estimate the model using a three-dimensional data set consisting of 25 characteristics of 1,342 mutual funds observed over 34 quarters. The tensor factor models reduce the data dimensionality by 97% while capturing 93% of the variation of the data. I relate higher-dimensional tensor models to standard two-dimensional models and show that the components of the model have clear economic interpretations. |
| 主题 | Econometrics ; Estimation Methods ; Financial Economics ; Portfolio Selection and Asset Pricing |
| URL | https://www.nber.org/papers/w29833 |
| 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/587506 |
| 推荐引用方式 GB/T 7714 | Martin Lettau. High-Dimensional Factor Models with an Application to Mutual Fund Characteristics. 2022. |
| 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 | ||
| w29833.pdf（869KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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