The time-series literature reports two stylized facts about output dynamics in the United States: GNP growth is positively autocorrelated, and GNP appears to have an important trend-reverting component. This paper investigates whether current real-business-cycle (RBC) models are consistent with these stylized facts. Many RBC models have weak internal propagation mechanisms and must rely on external sources of dynamics to replicate both facts. Models that incorporate labor adjustment costs are partially successful. They endogenously generate positive autocorrelation in output growth, but they need implausibly large transitory shocks to match the trend-reverting component in output.

This paper argues that UK WWI fiscal policy followed the ‘English method’identified by Sprague (1917) and his discussants, and revived by the US tofinance the Korean War (see Ohanian 1997). During WWI, UK fiscal policyadopted the “McKenna rule” named for Reginald McKenna, Chancellor ofthe Exchequer (1915-16). McKenna presented his fiscal rule to Parliamentin June 1915. The McKenna rule guided UK fiscal policy for the rest ofWWI and the interwar period. We draw on narrative evidence to show thatmotivation for the McKenna rule came from a desire to treat labour and cap-ital fairly and equitably, not pass WWI costs onto future generations, andcommit to a debt retirement path and higher taxes. However, a permanentincome model suggests the McKenna rule adversely affected the UK becausea higher debt retirement rate produces a lower consumption-output ratio.Data from 1916-37 supports this prediction. ∗ The views in this paper represent those of the authors and are not necessarily those ofthe Federal Reserve Bank of Atlanta, the Federal Reserve System, the Reserve Bankof New Zealand, Norges Bank, or their respective staffs. We thank Ellis Tallman formany useful comments and Kateryna Rakowsky for research assistance. A version ofthis paper is forthcoming in the Papers and Proceedings of the American EconomicReview.

This paper introduces the model confidence set (MCS) and applies it to the selection of models. A MCS is a set of models that is constructed such that it will contain the best model with a given level of confidence. The MCS is in this sense analogous to a confidence interval for a parameter. The MCS acknowledges the limitations of the data, such that uninformative data yield a MCS with many models, whereas informative data yield a MCS with only a few models. The MCS procedure does not assume that a particular model is the true model; in fact, the MCS procedure can be used to compare more general objects, beyond the comparison of models. We apply the MCS procedure to two empirical problems. First, we revisit the inflation forecasting problem posed by Stock and Watson (1999), and compute the MCS for their set of inflation forecasts. Second, we compare a number of Taylor rule regressions and determine the MCS of the best regression in terms of in-sample likelihood criteria.