The rapid spread of COVID-19 across the globe primed a variety of non-pharmaceutical interventions (NPIs). Given these NPIs, whether the SIR parameters followed a Bayesian learning, a random walk pattern or other type of learning with evolving epidemiological data over time has implications for...

We estimate the effects of domestic and foreign quantitative easing (QE) programmes on a small open economy, Sweden, using a structural BVAR model. Domestic QE raised GDP, lowered unemployment and depreciated the currency, while effects on in ation are less clear. The ECB QE had large positive...

Intraday high-frequency data of stock returns exhibit not only typical characteristics (e.g., volatility clustering and the leverage effect) but also a cyclical pattern of return volatility that is known as intraday seasonality. In this paper, we extend the stochastic volatility (SV) model for...

Knowledge management within organizations allows to support a global business strategy and represents a systemic and organized attempt to use knowledge within an organization to improve its performance. The objective of this research is to study and analyze knowledge management through Bayesian...

We consider both frequentist and empirical Bayes forecasts of a single time series using a linear model with T observations and K orthonormal predictors. The frequentist formulation considers estimators that are equivariant under permutations (reorderings) of the regressors. The empirical Bayes...

This paper analyzes the monetary policy of the Federal Reserve (Fed) and the Bank of England (BoE) both before and after the global financial crisis. In contrast to extant literature, we use a Bayesian model averaging (BMA) approach that does not rely on one underlying empirical model but...

I introduce a simple Bayesian methodology to analyze citation metrics across fields of Mathematics. I collect and analyze the MathSciNet http://www.ams.org/mathscinet} profiles of Full Professors of Mathematics at all 131 R1, research oriented US universities. The data recorded was citations,...

In this paper, we study a general model of information acquisition: costly Bayesian learning. Using a menu choice framework, we provide an axiomatic characterization of the model, identify its parameters (a utility function, an increasing transformation, a second-order prior belief, and an...

We present a general methodology based on skew t copulas and Bayesian inference for modelling extreme multivariate dependent losses and the regulatory capital for operational risk. Current approaches fail to model both asymmetric dependence and accurate extreme upper tail dependence (e.g. 99.9%...

We introduce a Bayesian Mixed-Frequency VAR model for the aggregate euro area labour market that features a structural identification via sign restrictions. The purpose of this paper is twofold: we aim at (i) providing reliable and timely forecasts of key labour market variables and (ii)...