Measuring the Output Gap Using Stochastic Model Specification Search

It is well known that different specification choices can give starkly different output gap
estimates. To account for model uncertainty, we average estimates over a wide variety
of popular specifications using stochastic model specification search. In particular, we
consider three types of specification choices: sets of variables used in the analysis,
output trend specifications and distributional assumptions. Using US data, we find that
the unemployment gap is useful in estimating the output gap, but conditional on the
unemployment gap, the inflation gap no longer depends on the output gap. Our results
show a steady decline in trend output growth throughout the sample, and the estimate at
the end of our sample is only about 1%. Moreover, data favor t over Gaussian distributed
innovations, suggesting the relatively frequent occurrence of extreme events.