Business cycle detrending of macroeconomic data via a latent business cycle index

We use Markov Chain Monte Carlo methods to augment a vector autoregressive system with a latent business cycle index that is negative during recessions and positive during expansions. We then sample counterfactual values of the macroeconomic variables in the case where the latent business cycle index is held constant at its median value. These counterfactual values represent posterior beliefs about how the economy would have evolved absent business cycle fluctuations. One advantage is that a VAR framework provides model-consistent counterfactual values in the same way that VARs provide model-consistent forecasts, so data series are not detrended in isolation from each other. We apply these methods to estimate the business cycle components of industrial production growth, consumer price inflation, the federal funds rate and the spread between long-term and short-term interest rates. These decompositions provide an explicitly counterfactual approach to deriving empirical business cycle facts that complements other approaches.