Measuring the output gap using stochastic model specification search

Vol: 
CAMA Working Paper 2/2017
Author name: 
Joshua C.C. Chan
Angelia L. Grant
Year: 
2017
Month: 
January
Abstract: 

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.

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