easily integrated into Bayesian estimation procedures like the Gibbs sampler. By allowing for incomplete data sets, the …

estimates. For Bayesian estimation, these identifying assumptions are usually implemented by imposing constraints on certain … Estimation ; Factor Models ; Multimodality ; Ordering Problem ; Orthogonal Transformation …

This paper considers factor estimation from heterogenous data, where some of the variables are noisy and only weakly … estimation with sparse priors on the loadings matrix. The choice of a sparse prior is an extension to the existing macroeconomic … majority of the variables in both datasets are irrelevant for factor estimation. -- Factor models ; variable selection ; sparse …

estimates. For Bayesian estimation, these identifying assumptions are usually implemented by imposing constraints on certain … Estimation ; Factor Models ; Multimodality ; Ordering ; Orthogonal Transformation …

estimates. For Bayesian estimation, these identifying assumptions are usually implemented by imposing constraints on certain …

This paper considers factor estimation from heterogenous data, where some of the variables are noisy and only weakly … estimation with sparse priors on the loadings matrix. The choice of a sparse prior is an extension to the existing macroeconomic … majority of the variables in both datasets are irrelevant for factor estimation …

estimation of static factor models and factor augmented autoregressions using a set of 190 quarterly observations of 144 US …

estimation of VARMAs is perceived to be challenging and proposed various ways to simplify it. Nevertheless, VARMAs continue to be …