# Econometrics of Machine Learning Methods in Economic Forecasting

arXiv: https://arxiv.org/abs/2308.10993 · HTML: https://mlsanity.com/paper/2308.10993/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 107 references that are in the map.
- First posted on arXiv 21 Aug 2023.

## Builds on (most cited first, 21 of 21)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [Deep Neural Networks for Estimation and Inference](https://mlsanity.com/paper/1809.09953/) (2018)
- [Machine Learning Advances for Time Series Forecasting](https://mlsanity.com/paper/2012.12802/) (2020)
- [Factor Models for High-Dimensional Tensor Time Series](https://mlsanity.com/paper/1905.07530/) (2019)
- [How is Machine Learning Useful for Macroeconomic Forecasting?](https://mlsanity.com/paper/2008.12477/) (2020)
- [High-Dimensional Granger Causality Tests with an Application to VIX and News](https://mlsanity.com/paper/1912.06307/) (2019)
- [Fair Policy Targeting](https://mlsanity.com/paper/2005.12395/) (2020)
- [Oracle Inequalities for High Dimensional Vector Autoregressions](https://mlsanity.com/paper/1311.0811/) (2013)
- [Rank Determination in Tensor Factor Model](https://mlsanity.com/paper/2011.07131/) (2020)
- [Is completeness necessary? Estimation in nonidentified linear models](https://mlsanity.com/paper/1709.03473/) (2017)
- [Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations](https://mlsanity.com/paper/1912.09002/) (2019)
- [Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application](https://mlsanity.com/paper/2008.03600/) (2020)
- [LASSO-Driven Inference in Time and Space](https://mlsanity.com/paper/1806.05081/) (2018)
- [Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure](https://mlsanity.com/paper/1902.10991/) (2019)
- [Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness](https://mlsanity.com/paper/2010.08463/) (2020)
- [Constrained Classification and Policy Learning](https://mlsanity.com/paper/2106.12886/) (2021)
- [The boosted HP filter is more general than you might think](https://mlsanity.com/paper/2209.09810/) (2022)
- [Panel Data Nowcasting: The Case of Price-Earnings Ratios](https://mlsanity.com/paper/2307.02673/) (2023)
- [Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions](https://mlsanity.com/paper/2301.10592/) (2023)

## Built on by (most cited first, 1 of 1)

- [Heatwave increases nighttime light intensity in hyperdense cities of the Global South: A double machine learning study](https://mlsanity.com/paper/2503.00557/) (2025)

## Abstract (arXiv)

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.