The great moderation: updated evidence with joint tests for multiple structural changes in variance and persistence

We assess the empirical evidence about the Great Moderation using a comprehensive framework to test for multiple structural changes in the coefficients and in the variance of the error term of a linear regression model provided by Perron, Yamamoto, and Zhou (2019). We apply it to U.S. real GDP and its major components for the period 1960:1 to 2018:4. A notable feature of our approach is that we adopt an unobserved component model, allowing for two breaks in the trend function in 1973:1 and 2008:1, in order to obtain a stationary or cyclical component modelled as an autoregressive process. First, we confirm evidence about the Great Moderation, i.e., a structural change in variance of the errors in the mid-80s for the various series. Second, additional breaks in variance are found in 1970:3 for GDP and production (goods), after which the sample standard deviation increased by three times. Hence, a part of the Great Moderation can be viewed as a reversion to the pre-70s level of volatility. Third, the evidence about systematic changes in the sum of the autoregressive coefficients (a measure of persistence) is weak over the whole sample period. Finally, we find little evidence of structural changes occurring in both the variance and the coefficients following the Great Recession (2007-2008). These results support views emphasizing the good luck hypothesis as a source of the Great Moderation, which continues even after the Great Recession.First author draf