| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w16634 |
| 来源ID | Working Paper 16634 |
| Identification and Inference in Linear Stochastic Discount Factor Models with Excess Returns | |
| Craig Burnside | |
| 发表日期 | 2010-12-23 |
| 出版年 | 2010 |
| 语种 | 英语 |
| 摘要 | When excess returns are used to estimate linear stochastic discount factor (SDF) models, researchers often adopt a normalization of the SDF that sets its mean to 1, or one that sets its intercept to 1. These normalizations are often treated as equivalent, but they are subtly different both in population, and in finite samples. Standard asymptotic inference relies on rank conditions that differ across the two normalizations, and which can fail to differing degrees. I first establish that failure of the rank conditions is a genuine concern for many well known SDF models in the literature. I also describe how failure of the rank conditions can affect inference, both in population and in finite samples. I propose using tests of the rank conditions not only as a diagnostic device, but also for model reduction. I show that this model reduction procedure has desirable size and power properties in a Monte Carlo experiment with a calibrated model. |
| 主题 | Econometrics ; Estimation Methods ; Financial Economics ; Portfolio Selection and Asset Pricing |
| URL | https://www.nber.org/papers/w16634 |
| 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/574308 |
| 推荐引用方式 GB/T 7714 | Craig Burnside. Identification and Inference in Linear Stochastic Discount Factor Models with Excess Returns. 2010. |
| 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 | ||
| w16634.pdf（910KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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