Gradient Boosting for Spatial Panel Models with Random and Fixed Effects

TL;DR

This paper introduces a flexible gradient boosting algorithm for spatial panel models with fixed and random effects, improving estimation accuracy and interpretability in high-dimensional spatial data analysis.

Contribution

It proposes a novel model-based gradient boosting method tailored for spatial panel data, capable of handling high-dimensional settings with variable selection and regularization.

Findings

Effective in low- and high-dimensional simulations

Improves prediction accuracy in real-world spatial data

Supports model and variable selection

Abstract

Due to the increase in data availability in urban and regional studies, various spatial panel models have emerged to model spatial panel data, which exhibit spatial patterns and spatial dependencies between observations across time. Although estimation is usually based on maximum likelihood or generalized method of moments, these methods may fail to yield unique solutions if researchers are faced with high-dimensional settings. This article proposes a model-based gradient boosting algorithm, which enables estimation with interpretable results that is feasible in low- and high-dimensional settings. Due to its modular nature, the flexible model-based gradient boosting algorithm is suitable for a variety of spatial panel models, which can include random and fixed effects. The general framework also enables data-driven model and variable selection as well as implicit regularization where the bias-variance trade-off is controlled for, thereby enhancing accuracy of prediction on out-of-sample spatial panel data. Monte Carlo experiments concerned with the performance of estimation and variable selection confirm proper functionality in low- and high-dimensional settings while real-world applications including non-life insurance in Italian districts, rice production in Indonesian farms and life expectancy in German districts illustrate the potential application.