Descriptions
Conditional forecasts, i.e. projections of a set of variables of interest on the future paths of some other variables, are used routinely by empirical macroeconomists in a number of applied settings, including in central banks. In spite of this, the existing algorithms used to generate conditional forecasts tend to be very computationally intensive, especially when working with large vector Autoregressions. The seminar will introduce a novel sampler that is fast by showing in a simulation study that the proposed method produces forecasts that are identical to those from the existing algorithms but in a fraction of the time. It will then illustrate the performance of the method in a large Bayesian vector autoregression on future trajectories of key US macroeconomic indicators over the 2020–2022 period.
Speaker:
Dr. Aubrey Poon is currently a Researcher at Örebro University, Sweden. He was previously a Research Associate at the University of Strathclyde, Glasgow. He is also a Research Associate at the Centre for Applied Macroeconomic Analysis (CAMA) at the Australian National University.
His field of research is in Applied Macroeconometrics, and particularly interested in the application of Bayesian estimation and computation of time-series and DSGE models.
