| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP11388 |
| DP11388 In-sample Inference and Forecasting in Misspecified Factor Models | |
| Barbara Rossi | |
| 发表日期 | 2016-07-12 |
| 出版年 | 2016 |
| 语种 | 英语 |
| 摘要 | This paper considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension reduction devices: principal compo- nents, Ridge, Landweber Fridman, and Partial Least Squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning parameter to be selected, we also propose data-driven selection methods based on cross- validation and establish their optimality. Monte Carlo simulations and an empirical application to forecasting ináation and output growth in the U.S. show that data-reduction methods out- perform conventional methods in several relevant settings, and might e§ectively guard against instabilities in predictorsíforecasting ability. |
| 主题 | Monetary Economics and Fluctuations |
| 关键词 | Forecasting Regularization methods Factor models Ridge Partial least squares Principal components Sparsity Large datasets Variable selection Gdp forecasts |
| URL | https://cepr.org/publications/dp11388 |
| 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/540201 |
| 推荐引用方式 GB/T 7714 | Barbara Rossi. DP11388 In-sample Inference and Forecasting in Misspecified Factor Models. 2016. |
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