G. Rosas, “Dynamic Latent Trait Models: An Application to Latin American Banking Crises”, Electoral Studies, 28 (special symposium on Measurement Methods for Better Longitudinal Modelling), 2009.
Dynamic latent trait models combine information from a variety of manifest variables, possibly measured on different scales, that are presumed to be indicators of an unobserved latent phenomenon, while allowing appropriate consideration of the longitudinal character of time series. I use a Bayesian dynamic latent trait model of banking sector financial accounts measured at the country/quarter level to build an indicator of banking system robustness in Latin America. As a methodological innovation, I extend dynamic latent trait models to take into account country-specific effects of bank regulatory regimes through hierarchical modeling of factor loadings. I suggest how these models can be applied to other types of phenomena—for example to combine existing political regime indicators to build a more informative measure of democracy.