WALS Estimation and Forecasting in Factor-Based Dynamic Models with an Application to Armenia

Abstract

Two model averaging approaches are used and compared in estimating and forecasting dynamic factor models, the well-known Bayesian model averaging (BMA) and the recently developed weighted average least squares (WALS). Both methods propose to combine frequentist estimators using Bayesian weights. We apply our framework to the Armenian economy using quarterly data from 2000–2010, and we estimate and forecast real GDP growth and inflation.