Using replication codes for the December 2013 and May 2014 vintages shared generously by Fernald, we assess the quantitative importance of the two changes. Table 2 reports the results. For comparison, the first and the last columns replicate the business cycle properties of the actual December 2013 and May 2014 vintages of estimated utilization growth from table 1. The second column, labeled $\Delta lnu^t13,BFFK$, shows the effect of switching to the industry weights and proportionality factors from Basu et al. (2013). While this switch lowers the volatility of utilization somewhat, it leaves the correlation with the 2007 vintage essentially unchanged. As shown by the third column, labeled $\Delta lnu^t13,BW$, by contrast, changing the detrending method from bandpass filtering to bi-weight filtering leads to a substantial increase in the volatility of utilization growth and a concurrent decrease in the correlation with the 2007 vintage. Finally, as shown in the fourth column, labeled $\Delta lnu^t13,BFFK&BW$, the two changes essentially replicate the 2014 vintage. The remaining difference is due to data revisions.

Table 2.

. | $\Delta lnu^t13$ . | $\Delta lnu^t13,BFFK$ . | $\Delta lnu^t13,BW$ . | $\Delta lnu^t13,BFFK&BW$ . | $\Delta lnu^t14$ . |
---|---|---|---|---|---|

Standard deviation | 2.94 | 2.26 | 4.76 | 3.69 | 3.75 |

Correlation with $\Delta lnu^t07$ | 0.94 | 0.91 | 0.59 | 0.57 | 0.58 |

. | $\Delta lnu^t13$ . | $\Delta lnu^t13,BFFK$ . | $\Delta lnu^t13,BW$ . | $\Delta lnu^t13,BFFK&BW$ . | $\Delta lnu^t14$ . |
---|---|---|---|---|---|

Standard deviation | 2.94 | 2.26 | 4.76 | 3.69 | 3.75 |

Correlation with $\Delta lnu^t07$ | 0.94 | 0.91 | 0.59 | 0.57 | 0.58 |

This table shows simulated utilization series based on the 2013 vintage data. See the text for details. The sample period for all statistics is 1947q3 to 2007q3.

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