A0161
Title: Non-linear time series models and machine learning
Authors: Nour Meddahi - Toulouse School of Economics (France) [presenting]
Abstract: There has been a rapid development of machine learning(ML) methods in econometrics and statistics, especially for forecasting purposes. For instance, ML methods have been recently used in several studies for forecasting economic and financial variables like asset returns, stock and bond returns, volatility, inflation), and macroeconomic variables. An important common conclusion of the studies is that ML methods are successful in forecasting because they account for non-linearities that popular time series models do not. The first goal is to highlight the non-linearities that ML methods capture and connect them with traditional non-linear time series modeling. The second goal of the paper is to modify some traditional non-linear time series model by including insights from the literature. Applications to the Euro-US dollar exchange rate and the SP500 index are provided.
