Measuring the Output Gap in Switzerland with Linear Opinion Pools

Abstract

We use the recently proposed linear opinion pool methodology of Garratt et al. (2014) to construct real-time ensemble nowcast densities of the output gap for Switzerland over an out-of-sample period from 2003:Q1 to 2015:Q4. The model space consists of a large number of bivariate VAR specifications for inflation and the output gap, with each specification using a different estimate of the output gap, lag order in the VAR, and structural break information. The ensemble nowcast densities for the output gap are constructed by combining the predictive densities of the individual VAR specifications, weighted by their ability to provide accurate density forecasts for inflation. The overall performance of the linear opinion pool is assessed by its real-time output gap nowcasts and by the size of the ex post revisions to the output gap nowcasts. We find that the linear opinion pool does not produce any more accurate density or point forecasts of inflation than a number of simple univariate benchmark models that condition on the same structural break information. Further, the linear opinion pool’s real-time estimate of the output gap is no more robust to ex post re visions than the real-time estimates of the individual univariate output gaps. The fact that Swiss GDP price deflator data are subject to large revisions, complicates the measurement and forecasting of inflation.