# Bayesian Nonlinear Regression using Sums of Simple Functions

arXiv: https://arxiv.org/abs/2312.01881 · HTML: https://mlsanity.com/paper/2312.01881/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [The Macroeconomy as a Random Forest](https://mlsanity.com/paper/2006.12724/) (2020)
- [Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs](https://mlsanity.com/paper/2008.12706/) (2020)
- [Bayesian Nonparametric Calibration and Combination of Predictive Distributions](https://mlsanity.com/paper/1502.07246/) (2015)
- [Can Machine Learning Catch the COVID-19 Recession?](https://mlsanity.com/paper/2103.01201/) (2021)
- [Inference in Bayesian Additive Vector Autoregressive Tree Models](https://mlsanity.com/paper/2006.16333/) (2020)
- [Bayesian nonparametric sparse VAR models](https://mlsanity.com/paper/1608.02740/) (2016)
- [Achieving Shrinkage in a Time-Varying Parameter Model Framework](https://mlsanity.com/paper/1611.01310/) (2016)
- [Bayesian nonparametric panel Markov-switching GARCH models](https://mlsanity.com/paper/2012.10124/) (2020)
- [Large Hybrid Time-Varying Parameter VARs](https://mlsanity.com/paper/2201.07303/) (2022)

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

- [Asymmetries in Financial Spillovers](https://mlsanity.com/paper/2410.16214/) (2024)

## Abstract (arXiv)

This paper proposes a new Bayesian machine learning model that can be applied to large datasets arising in macroeconomics. Our framework sums over many simple two-component location mixtures. The transition between components is determined by a logistic function that depends on a single threshold variable and two hyperparameters. Each of these individual models only accounts for a minor portion of the variation in the endogenous variables. But many of them are capable of capturing arbitrary nonlinear conditional mean relations. Conjugate priors enable fast and efficient inference. In simulations, we show that our approach produces accurate point and density forecasts. In a real-data exercise, we forecast US macroeconomic aggregates and consider the nonlinear effects of financial shocks in a large-scale nonlinear VAR.