| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP14066 |
| DP14066 Competing Models | |
| José Luis Montiel Olea; Pietro Ortoleva; Mallesh Pai; Andrea Prat | |
| 发表日期 | 2019-10-19 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | We develop a model in which different agents compete to predict a variable of interest. This variable is related to observables via an unknown data generating process. All agents are Bayesian, but may have ‘misspecified models’ of the world, i.e., they consider different subsets of observables to make their prediction. After observing a common dataset, who has the highest confidence in her predictive ability? We characterize it and show that it crucially depends on the size of the dataset. With big data, we show it is typically ‘large dimensional,’ possibly using more variables than the true model. With small data, we show (under additional assumptions) that it is an agent using a model that is ‘small-dimensional,’ in the sense of considering fewer covariates than the true data generating process. The theory is applied to auctions of assets where bidders observe the same information but hold different priors. |
| 主题 | Industrial Organization |
| URL | https://cepr.org/publications/dp14066 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/542951 |
| 推荐引用方式 GB/T 7714 | José Luis Montiel Olea,Pietro Ortoleva,Mallesh Pai,et al. DP14066 Competing Models. 2019. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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