Agnostic output gap estimation and decomposition in large cross-sections

Abstract

This paper uses a Bayesian non-stationary dynamic factor model to extract common trends and cycles from large datasets. An important but neglected feature of Bayesian statistics allows to treat stationary and non-stationary time series equally in terms of parameter estimation. Based on this feature we show how to extract common trends and cycles from the data by ex-post processing the posterior output and describe how to derive an agnostic output gap measure. We apply the procedure to a large panel of quarterly time series that covers 158 macroeconomic and financial series for the United States. We find that our derived output gap measure tracks the U.S. business cycle well, exhibiting a high correlation with alternative estimates of the output gap. Since the factors are extracted from a comprehensive dataset, the resulting output gap estimates are stable at the current edge and can be decomposed in a new and meaningful way.