Essays on methods for forecasting using high dimension data
Creators: name: Liu, Yang
Publisher: name: No Publisher Supplied
Year: 2024

description: With the advent of big data and machine learning methods (ML), economists can utilize more information from high dimensional and high frequency dataset for financial analysis and economic forecasting. This dissertation consists of two essays that explore issues in empirical macroeconomic forecasting using machine learning methods to construct a variety of latent factors and economic uncertainty indexes from high dimensional dataset. In the first essay, we construct several new business conditions predictors and economic uncertainty indexes for use in improving the forecast accuracy of macroeconomic variables. In the second essay, we introduce a novel variable selection procedure that correctly distinguishes between relevant and irrelevant variables to be used when estimating latent factors in a factor augmented vector autoregressive framework. In Chapter 2, we evaluate the marginal predictive content of a variety of new business conditions (BC) predictors as well as nine economic uncertainty indexes (EUIs) constructed using these predictors. Our predictors are defined as selected observable variables and latent factors extracted from a high dimensional macroeconomic dataset and our EUIs are functions of predictive errors from models that incorporate these predictors. Estimation of the predictors is based on a number of extant and novel machine learning methods that combine dimension reduction and shrinkage. When predicting 14 monthly U.S. economic series selected from 8 different groups of economic variables, the new indexes and predictors are shown to result in significant improvements in forecast accuracy, relative to predictions made using benchmark models. Moreover, while inclusion of either BC predictors or EUIs often yields forecast accuracy improvements, greater predictive gains accrue when using BC predictors with real economic activity type variables. Also, adding both BC predictors and EUIs together is particularly useful when forecasting housing market variables at short horizons. In Chapter 3, we propose a new variable selection method that allows researchers to distinguish between variables that are relevant in the sense that they provide useful information for estimating underlying latent factors and variables that are irrelevant in the sense that they do not load on underlying factors, in an FAVAR model. In our context, variable selection methods are needed because using too many irrelevant variables may lead to inconsistency in factor estimation. Our procedure is designed to facilitate consistent factor estimation and can be viewed as the factor model analog of the type of multiple hypothesis testing or variable selection procedures that people use to select regressors when specifying linear regression. One key difference between our method and the typical multiple hypothesis testing procedure used in the literature is that rather than controlling the overall Type I error at some fixed non-zero level, our procedure is completely consistent in the sense that the probability of both Type I and Type II errors goes to zero asymptotically as sample sizes approach infinity. Monte Carlo evidence indicates that our method has very good finite sample properties. Additionally, we analyze a real-time macroeconomic dataset, where it is shown that our method delivers factors that result in improved marginal predictive content, relative to cases where standard principal components as well as hard-thresholding methods are used in factor estimation.