Forecasting with Dynamic Panel Data Models

University Of Southern California, Los Angeles CA

Investigators

Abstract

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.