Detecting temporal anomalies with pseudo age groups: Homeownership in Canada, 1981 to 2016

Abstract

Methodological advances in demographic research, especially age-period-cohort (APC) analysis, primarily focus on developing new models yet often fail to consider practical concerns in empirical analysis. We propose a mixed approach that integrates multiple data imputation and structural change analysis in time series so that scholars can (i) construct pseudo age groups based on more coarsely grouped age data and (ii) identify temporal anomalies. This approach is illustrated using multiple waves of Canadian Population Census data . We construct pseudo age groups based on more coarse age information available in the Census data and identify a local anomaly in the temporal trajectory of homeownership in Canada's less populous provinces and territories. These findings are assessed and validated in comparison with results from more populous Canadian provinces. This research broadens the methodological repertoire for demographers, geographers, and social scientists in general and extends the literature on homeownership in an understudied area.

A manifesto for computational sociology: The Canadian perspective

“…Computational methods include a gamut of different techniques including machine learning (e.g., deep learning, statistical learning, reinforcement learning), social network analysis, text and data mining (e.g., sentiment analysis, topic modelling, named‐entity recognition), agent‐based modelling, more flexible regression/estimation models (e.g., regression shrinkage and selection, Bayesian statistics, spatial regression models), advances in survey methods (e.g., survey experiments, optimum design, respondent‐driven sampling), and so on. Some sociologists in Canada have contributed directly to the development of particular methods (Alexander & Alkema, 2021; Andersen, 2008; Bignami‐Van Assche et al., forthcoming; Fosse & Winship, 2019; Fox, 2015; Fox & Andersen, 2006; Fu et al., 2020, 2021; Hayduk, 1996; Li et al., forthcoming; Miles, 2016; Nelson, 2020; Stecklov et al., 2018; Wellman et al., 2003, 2020), but more often sociologists have embraced and adapted methods developed by computer scientists, statisticians, and econometricians (Abul‐Fottouh et al., 2020; Boase, 2016; Das, 2022; Gallupe et al., 2019; Gruzd & Mai, 2020; Gu et al., 2021; Hogan & Berry, 2011; Howe et al., forthcoming; Kudla & Parnaby, 2018; Letarte et al., 2021; Li & Luo, 2020; McLevey, 2022; McMahan & McFarland, 2021; Quan‐Haase et al., 2021; Richardson et al., 2021; Roth et al., forthcoming; Shor & Miltsov, 2020; Shor et al., 2013; Silver & Silva, 2021; Smith, 2020; Sytsma et al., 2021; Veenstra & Vanzella‐Yang, 2022; Yuan et al., 2022).…”