Econometrics of Machine Learning Methods in Economic Forecasting

TL;DR

This paper reviews recent machine learning techniques applied to economic forecasting, highlighting advances in nowcasting, textual data analysis, high-dimensional causality tests, and validation methods to improve forecast accuracy.

Contribution

It provides a comprehensive survey of recent machine learning methods in economic forecasting, emphasizing new techniques and applications in the field.

Findings

Enhanced nowcasting accuracy with machine learning methods

Integration of textual data improves economic predictions

Advanced high-dimensional causality tests facilitate better understanding of economic relationships

Abstract

This paper surveys the recent advances in machine learning method for economic forecasting. The survey covers the following topics: nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validation, classification with economic losses.