| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w25102 |
| 来源ID | Working Paper 25102 |
| Forecasting with Dynamic Panel Data Models | |
| Laura Liu; Hyungsik Roger Moon; Frank Schorfheide | |
| 发表日期 | 2018-10-01 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | This paper considers the problem of forecasting a collection of short time series using cross sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This formula utilizes cross-sectional information to transform the unit-specific (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coefficients. We show that the risk of a predictor based on a non-parametric kernel estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlated-random-effects distribution as known (ratio-optimality). Our empirical Bayes predictor performs well compared to various competitors in a Monte Carlo study. In an empirical application we use the predictor to forecast revenues for a large panel of bank holding companies and compare forecasts that condition on actual and severely adverse macroeconomic conditions. |
| 主题 | Econometrics ; Estimation Methods ; Financial Economics ; Financial Institutions |
| URL | https://www.nber.org/papers/w25102 |
| 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/582775 |
| 推荐引用方式 GB/T 7714 | Laura Liu,Hyungsik Roger Moon,Frank Schorfheide. Forecasting with Dynamic Panel Data Models. 2018. |
| 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 | ||
| w25102.pdf（1646KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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