Determining potential output and the output gap-two inherently unobservable variables-is a major challenge for macroeconomists. This paper presents the R package sectorgap, which features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying...

Determining potential output and the output gap-two inherently unobservable variables-is a major challenge for macroeconomists. This paper presents the R package sectorgap, which features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying...

This note describes how the Kalman filter can be modified to allow for the vector of observables to be a function of lagged variables without increasing the dimension of the state vector in the filter. This is useful in applications where it is desirable to keep the dimension of the state vector...

A Bayesian Markov Chain Monte Carlo methodology is developed for estimating the stochastic conditional duration model. The conditional mean of durations between trades is modelled as a latent stochastic process, with the conditional distribution of durations having positive support. The sampling...