This paper illustrates the importance of consistency between the empirical measurement and the concept of variables in macroeconomic models. Since standard New Keynesian models do not account for demographic trends and sectoral shifts, I propose adjusting hours per capita used to estimate such models to enhance the consistency between the data and the model. Without this adjustment, low frequency shifts in hours lead to unreasonable trends in the output gap, caused by the close link between hours and the output gap in such models. The retirement wave of baby boomers, for example, lowers U.S. aggregate hours per capita, which leads to erroneous permanently negative output gap estimates following the Great Recession. After correcting hours for changes in the age composition, the estimated output gap closes gradually instead following the years after the Great Recession.
low frequency trends demographic trends hours per capita measurement output gap estimates DSGE models Bayesian estimation