Forecasting with Dynamic Panel Data Models

样本外预测是数据科学的基本课题之一。传统的预测方法需要长时间序列的经济数据才能实现，不能处理时间跨度较短的经济数据，如预测近期金融危机后的初创公司业绩和银行业绩。该项目旨在开发一种新的方法，利用面板数据中的横截面信息来生成对短时间序列经济数据的预测。在这样的预测中，“好的”预测不仅需要对面板数据中的共同成分进行“好的”估计，还需要对面板模型中的个别特定成分进行“好”的估计，这是该项目的主要挑战。研究人员将开发基于统计决策理论的最优预测，并研究如何实施它。最终，该项目将提供更好的预测工具，帮助个人决策者和政策制定者。为了开发新的方法来生成对短时间序列经济数据的预测，调查者将考虑一个具有未观察到的个体异质性的线性动态面板模型。由于对面板数据的“好”预测不仅需要对面板模型中的公共参数进行“好的”估计，而且还需要对面板模型中的个别特定参数进行“好的”估计，因此它们通常不能与短时间跨度的面板一致地估计。现有的文献大多集中于在大维个体特定参数存在的情况下建立公共参数的良好估计，而不一定是建立个体特定参数的良好估计。这个项目与以前文献的关键不同之处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。基于贝叶斯后验推断，调查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，包括预测美国银行在2008年金融危机后的表现。

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.