Tuning in RBC Growth Spectra

For US postwar data, the paper explains an array of RBC puzzles by adding to the standard RBC model external margins for both physical capital and human capital, and examining model fit with data across business cycle (BC) and low frequency (LF) as well as Medium Cycle (MC) windows. DSGE model. A novel metric of fi t, along with a uniform grid search to fit a wide array of moments. Measures of fit are presented by frequency window and moment category. For US postwar data, the paper explains an array of RBC puzzles by adding to the standard RBC model external margins for both physical capital and human capital, and examining model fi t with data across business cycle (BC) and low frequency (LF) as well as Medium Cycle (MC) windows. The model results in a goods sector productivity shock with a 7500 times smaller variance than the standard RBC model, implying greatly improved ampli cation of the shock. In addition, output growth persistence autocorrelation pro les are modeled as in data, thus improving upon the propagation puzzle. The model produces a consumption-output ratio as in the business cycle data, a labor share of output that is countercyclic as in data, and human capital investment time that is countercyclic as in data. Also the capacity utilization rate is procyclic within BC, LF and MC windows as in data; including labor moments, a wide array of moments are explained for correlations, volatilities and growth persistence across these business cycle and lower frequency windows. Using a metric of fit, along with a uniform grid search, measures of fi t are presented by window and category. In the BC window, key correlations have only an average 15% deviation from the data moments; the LF growth persistence has only an average 8% deviation from the data moments.