Factor modeling is a popular strategy to induce sparsity in multivariate models as they scale to higher dimensions. We develop Bayesian inference for a recently proposed latent factor copula model, which utilizes a pair copula construction to couple the variables with the latent factor. We use...

We propose a dynamic factor model which we use to analyze the relationship between education participation and national unemployment, as well as to forecast the number of students across the many different types of education. By clustering the factor loadings associated with the dynamic...

We propose a dynamic factor model which we use to analyze the relationship between education participation and national unemployment, as well as to forecast the number of students across the many different types of education. By clustering the factor loadings associated with the dynamic...

Asset returns exhibit grouped heterogeneity, and a “one-size-fits-all” model has been elusive empirically. This paper proposes a Bayesian Clustering Model (BCM) combining Bayesian factor selection and panel tree for asset clustering. The Bayesian model marginal likelihood guides the tree...

The biopharmaceutical sector is of considerable interest during the COVID-19 pandemic. This study aims to investigate the biopharmaceutical sector using the Shenwan Industry Classification and provides insights into investment strategies. We combine factor and cluster analyses to reduce data...

Systems of civil service reflect specific needs, traditions and institutional fundamentals of every state. The paper provides an outline of the European context of civil service, characteristics of basic features of civil service in EU countries, and evaluation of selected determinants of civil...

This paper presents a variance decomposition method - factor analysis with Procrustes rotation - that is capable of separating the global, regional and idiosyncratic components of various financial market indicators. The method is applied to indicators of five key financial markets: sovereign...

High dimensional composite index makes experts' preferences in setting weights a hard task. In the literature, one of the approaches to derive weights from a data set is Principal Component or Factor Analysis that, although conceptually different, they are similar in results when FA is based on...

The paper presents forecasts of headline and core inflation in Estonia with factor models in a recursive pseudo out-of-sample framework. The factors are constructed with a principal component analysis and are then incorporated into vector autoregressive (VAR) forecasting models. The analyses...