In this work, we rely on unconventional data sources to nowcast the year-on-year growth rate of Finnish indus-trial production, for different industries. As predictors, we use real-time truck traffic volumes measured automatically in different geographical locations around Finland, as well as...

Estimating economic earnings mobility is imperative for understanding the degree to which low pay employment is a temporary or long-term position. The current literature estimates transition probabilities between low and higher pay. This study extends the focus to identify the underlying...

We propose a new methodology to select a subset of assets for (partial) index replication, based on the latest research on factor models of large dimensions. Our method selects the set of leader stocks that can fully capture the systematic risk of the index to be replicated. Our selection...

Factor modelling extracts common information from a high-dimensional data set into few common components, where the latent factors usually explain a large share of data variation. Exploratory factor estimation induces sparsity into the loading matrix to associate units or series with those...

We measure the effectiveness of policies in a mixed frequency spatial VAR (MF-SVAR) modeling framework, conditional on the spillover and diffusion effects of the global pandemic and unemployment. Specifically, we examine two aspects of policy effectiveness from a spatio-temporal perspective,...

In this paper we propose a hierarchical shrinkage approach for multi-country VAR models. In implementation, we consider three different scale mixtures of Normals priors — specifically, Horseshoe, Normal- Gamma, and Normal-Gamma-Gamma priors. We provide new theoretical results for the...

In the context of latent factor models that are widely used in economics, a common assumption made is one of factor pervasiveness, which implies that all available predictor or informative variables in a dataset, with the possible exception of a negligible number of them, load significantly on...

Factor modelling extracts common information from a high-dimensional data set into few common components, where the latent factors usually explain a large share of data variation. Exploratory factor estimation induces sparsity into the loading matrix to associate units or series with those...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...