Work

Using GAMs to understand whether and how processes vary over space and time

Year: 2025

Type: conference-paper

Abstract: This paper an informed approach for constructing varying coefficient regression models using Generalized Additive Models (GAMs) with Gaussian Process (GP) smooths. Using a house price data over 13 years for a local case study, it investigates different model forms in order to determine the most probable model given the data. It determines the presence of any space-time dependencies between the target and each predictor variable is present, and if so their nature (ie whether they are independent or interact). It does this to avoid assumptions about the nature of spatial and / or temporal dependencies, in contrast to many existing approaches for space-time regressions which implicitly include baked in assumptions assumptions about the presence of these. The analysis uses tools in the stgam R package to undertake the analysis and to extract the varying coefficients from the model and a number of areas of further work are identified.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15225161

Authors: Comber, Alexis, Harris, Rich, Brunsdon, Chris

Institutions: University of Leeds, Rothamsted Research, National University of Ireland, Maynooth

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W6949547215

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