General Spatio-Temporal Factor Models for High-Dimensional Random Fields on a Lattice

TL;DR

This paper introduces a new spatio-temporal factor model (GSTFM) for high-dimensional random fields, providing a probabilistic foundation, estimation methods, and theoretical guarantees, with demonstrated advantages over existing models.

Contribution

The paper develops GSTFM, a novel dimensionality reduction approach for high-dimensional spatio-temporal data, including estimation procedures, theoretical properties, and an information criterion for factor number selection.

Findings

GSTFM accurately captures spatio-temporal covariance structures.

The estimator is consistent with proven convergence rates.

Synthetic data shows GSTFM outperforms existing models ignoring spatial correlations.

Abstract

Motivated by the need for analysing large spatio-temporal panel data, we introduce a novel dimensionality reduction methodology for $n$-dimensional random fields observed across a number $S$ spatial locations and $T$ time periods. We call it General Spatio-Temporal Factor Model (GSTFM). First, we provide the probabilistic and mathematical underpinning needed for the representation of a random field as the sum of two components: the common component (driven by a small number $q$ of latent factors) and the idiosyncratic component (mildly cross-correlated). We show that the two components are identified as $n\to\infty$. Second, we propose an estimator of the common component and derive its statistical guarantees (consistency and rate of convergence) as $\min(n, S, T )\to\infty$. Third, we propose an information criterion to determine the number of factors. Estimation makes use of Fourier analysis in the frequency domain and thus we fully exploit the information on the spatio-temporal covariance structure of the whole panel. Synthetic data examples illustrate the applicability of GSTFM and its advantages over the extant generalized dynamic factor model that ignores the spatial correlations.