Year: 2026
Type: article
Abstract: Econometric estimation of panel data models by feasible generalized least squares (FGLS) provides an example of how conceptually simple problems may run into computational bottlenecks. I address the main computational tasks of FGLS within the R system for statistical computing, comparing different tools from the point of view of computational efficiency. I concentrate on estimating two models: the popular “random effects” with two error components and the less restrictive “general GLS” specification, which does not fit into the standard computational framework usually employed for the former. I compare the standard solution (partial time demeaning) with two alternative strategies, based respectively on algebraic properties and on object-oriented programming. I show how, while naive implementations become infeasible with large datasets, both list operators and object-oriented matrix routines available in the R environment make the problem tractable for most practically relevant sample sizes on any machine. I conclude by briefly discussing the parallelization of critical tasks.
Source: Mathematics
DOI: 10.3390/math14122163
Author: Giovanni Millo
Institution: University of Trieste
Language: English
Topic: Data Analysis with R
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7165056674