Forecasting with Dynamic Panel Data Models

使用动态面板数据模型进行预测

• 批准号: 1625586
• 负责人: Hyungsik Moon
• 金额: $ 18.07万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1625586&HistoricalAwards=false
• 关键词: Forecasting; Dynamic; Panel; Data; Models

Out-of-sample prediction or forecasting is one of the fundamental subjects of data science. The conventional forecasting methods require long time series economic data for implementation, and cannot handle economic data with short time span such as predicting, for example, start-up companies' performances and bank performances after the recent financial crises. This project aims at developing a new method to generate forecasts for short time series economic data using cross sectional information in panel data. In such forecasting, "good" forecasts require not only "good" estimates of the common components in the panel data but also the individual specific components in the panel model, which is the main challenge of the project. The investigator will develop the optimal forecast based on statistical decision theory and investigate how to implement it. Ultimately, this project will provide better forecasting tools to help individual decision makers and policymakers. To develop new methods to generate forecasts for short time series economic data, the investigator will consider a linear dynamic panel model with unobserved individual heterogeneity. Because "good" forecasts for panel data require not only "good" estimates of the common parameters but also the individual specific parameters in the panel model, they cannot be estimated consistently with short time span panel in general. The existing literature mostly focuses on establishing good estimates of the common parameters in the presence of the large dimensional individual specific parameters, but not necessarily on establishing good estimates of the individual specific parameters. The key departure of this project from the previous literature is to relate the conditional mean of the unobserved individual effect variable to the posterior mean of the individual effect variable in the Bayesian framework. Building on Bayesian posterior inference, the investigator will derive an optimal forecast formula and develop several estimation methods to implement the optimal forecast. This project will further investigate interesting empirical applications, including forecasting the performances of US banks after 2008 financial crisis.

样本外预测或预测是数据科学的基础学科之一。传统的预测方法需要较长时间序列的经济数据才能实现，无法处理时间跨度较短的经济数据，例如预测近期金融危机后的创业公司业绩和银行业绩。本项目旨在开发一种新方法，利用面板数据中的横截面信息对短时间序列经济数据进行预测。在这样的预测中，“好的”预测不仅需要对面板数据中的共同组成部分进行“好的”估计，而且需要对面板模型中的个别特定组成部分进行“好的”估计，这是该项目的主要挑战。研究者将根据统计决策理论制定最优预测并研究如何实现它。最终，该项目将提供更好的预测工具，以帮助个别决策者和政策制定者。为了开发新的方法来对短时间序列经济数据进行预测，研究者将考虑一个未观察到个体异质性的线性动态面板模型。由于面板数据的“良好”预测不仅需要对共同参数进行“良好”估计，而且还需要对面板模型中的个别特定参数进行“良好”估计，因此它们无法与一般短时间跨度面板一致地进行估计。现有文献大多侧重于在存在大维度个体特定参数的情况下建立对共同参数的良好估计，而不一定建立对个体特定参数的良好估计。本项目与以往文献的关键区别在于将未观察到的个体效应变量的条件均值与贝叶斯框架中个体效应变量的后验均值联系起来。在贝叶斯后验推理的基础上，研究者将推导出最优预测公式，并开发几种估计方法来实现最优预测。该项目将进一步研究有趣的实证应用，包括预测2008年金融危机后美国银行的表现。