Estimating the Output Gap with High-Dimensional Time Series

The output gap measures the deviation of observed output from its potential, thereby defining imbalances in the real economy that affect utilization of resources and price inflation. A novel estimator of the output gap is proposed. It is based on a dynamic factor model that extracts from a high-dimensional set of time series the common component of a stationary transformation of the individual series. The latter results from the application of a nonlinear gap filter, such that for each of the individual time series the gap filter removes from the current value the historical local maximum, which in turn defines the potential. The smooth generalized principal components are extracted and the resulting common components are aggregated into a global output gap measure. An application is presented dealing with the U.S. industrial sector, where the proposed measure is constructed using the disaggregated market and industry groups time series. An evaluation of its external validity is conducted in comparison to alternative measures.