A frequent challenge when using graphical models in applications is that the sample size is limited relative to the number of parameters to be learned. Our motivation stems from applications where one has external data, in the form of networks between variables, that provides valuable...

This paper provides evidence that shifts in the occupational composition of the U.S. workforce are the most important factor explaining the trend decline in the labor share over the past four decades. Estimates suggest that while there is unitary elasticity between equipment capital and...

In this paper, we develop and apply Bayesian inference for an extended Nelson-Siegel (1987) term structure model capturing interest rate risk. The so-called Stochastic Volatility Nelson-Siegel (SVNS) model allows for stochastic volatility in the underlying yield factors. We propose a Markov...