Essays in macroeconomic forecasting and model evaluation

This dissertation studies forecasting model specification, estimation, prediction, and evaluation in big data environments. In an effort to contribute to the discussions of macroeconomic forecasting, I examine the studies of forecasting model specification and forecast accuracy testings and introduce new methodologies in empirical frameworks. The whole set-up of forecasting model specification and forecasting evaluation framework is a continuum of decisions, which can lead to different forecasting results. In closely-connected two papers, I attempt to empirically evaluate the implications of using different methodologies throughout all stages of macro forecasting and provide insightful conclusions for future researches in the literature. Chapter 2 revisits the question of predictive accuracy testing and model selection, and asks the question: does the loss function really matter, and if so, what can be gained when utilizing loss function-free model comparison and selection tests? So far in forecasting literature, forecasting results have been compared based on moment-based approaches which mostly concern about only first and second moment of forecasting errors and require to choose a loss function to begin with, which is an additional decisional problem. In Chapter 2, I compare forecasting results based on a distributional comparison approach suggested by Jin et al. (2016), which is technically based on the stochastic dominance principles and loss-function robust. A series of empirical experiments are carried out using macroeconomic time series data modeled using big data methods, including a large number of dimension reduction, shrinkage, and machine learning methods. Analysis and ranking of these methods is found to depend crucially on whether loss function dependent evaluation of their accuracy is carried out, or not. Chapter 3 builds on my first chapter by focusing on the usefulness of so called “supervised” approaches to forecast model selection in big-data environments. When constructing forecasting models using latent factor variables that are designed to condense information from large datasets into a small set of useful explanatory variables, standard approaches involve extracting information relevant to the entire dataset, and not targeted to a particular variable being forecasted. Supervised approaches to model specification do not do this, but instead penalize model specifications according to metrics designed to focus on the particular target variable(s) of interest. In order to evaluate the efficacy of supervised approaches, I carry out Monte Carlo simulations and empirical exercises and empirical results suggest that supervised approaches that are geared for the purpose of forecasting do serve its own purpose.Ph.D.Includes bibliographical referencesby Sungkyung Le