Forecasting The Future: Utilizing the Statistical Jump Model to Forecast GDP Output of the US Economy

Abstract

The dynamic macroeconomic environment today has placed a spotlight on the importance of economic forecasting. In the last few decades, many mathematical and econometric models have been developed to forecast economic performance; these models include the univariate and multivariate Autoregression models. One particularly interesting model is called the Markov Switching model, which falls under a subset of models called regime-switching models. Regime-switching models are models whose parameters depend on a series of homogeneous regimes. However, Markov switching models have many fundamental drawbacks, most notably its time-varying transition probabilities. Recently, a lot of research has been dedicated towards another regime-switching model called the Statistical Jump Model. The Statistical Jump Model minimizes an objective function, which consists of a loss function and a penalization term, and builds upon the Markov Switching Model. In the past, the Statistical Jump Model has been geared towards equity markets. This thesis seeks to explore the applicability of the Statistical Jump Model in forecasting US economic performance. We propose three methodologies to forecast US GDP using the Statistical Jump Model, which includes the utilization of boosting methods and external economic indicators to create a well-informed forecast. We conclude by benchmarking the our proposed models against common models in the literature.