| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP12716 |
| DP12716 Quantile Factor Models | |
| Juan Dolado; Liang Chen; Jesus Gonzalo | |
| 发表日期 | 2018-02-12 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | Quantile factor models (QFM) represent a new class of factor models for high-dimensional panel data. Unlike approximate factor models (AFM), which only extract mean factors, QFM also allow unobserved factors to shift other relevant parts of the distributions of observables. We propose a quantile regression approach, labeled Quantile Factor Analysis (QFA), to consistently estimate all the quantile-dependent factors and loadings. Their asymptotic distributions are established using a kernel-smoothed version of the QFA estimators. Two consistent model selection criteria, based on information criteria and rank minimization, are developed to determine the number of factors at each quantile. QFA estimation remains valid even when the idiosyncratic errors exhibit heavy-tailed distributions. An empirical application illustrates the usefulness of QFA by highlighting the role of extra factors in the forecasts of US GDP growth and inflation rates using a large set of predictors. |
| 主题 | Financial Economics |
| URL | https://cepr.org/publications/dp12716-0 |
| 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/541528 |
| 推荐引用方式 GB/T 7714 | Juan Dolado,Liang Chen,Jesus Gonzalo. DP12716 Quantile Factor Models. 2018. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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