Developments in life expectancy and the growing emphasis on biological and ‘healthy’ aging raise a number of important questions for health scientists and economists alike. Is it preferable to make lives healthier by compressing morbidity, or longer by extending life? What are the gains from targeting aging itself compared to efforts to eradicate specific diseases? Here we analyze existing data to evaluate the economic value of increases in life expectancy, improvements in health and treatments that target aging. We show that a compression of morbidity that improves health is more valuable than further increases in life expectancy, and that targeting aging offers potentially larger economic gains than eradicating individual diseases. We show that a slowdown in aging that increases life expectancy by 1 year is worth US$38 trillion, and by 10 years, US$367 trillion. Ultimately, the more progress that is made in improving how we age, the greater the value of further improvements.

In this paper we introduce identifying restrictions into a Markov-switching vector autoregression model. We define a separate set of impulse responses for each Markov regime to show how fundamental disturbances affect the variables in the model GHSHQGHQW on the regime. We go to illustrate the use of these regimedependent impulse response functions in a model of the U.S. economy. The regimes we identify come close to the "old" and "new economy" regimes found in recent research. We provide evidence that oil price shocks are much less contractionary and inflationary than they used to be. We show furthermore that the decoupling of the US economic performance from oil price shocks cannot be explained by "good luck" alone, but that structural changes within the US economy have taken place.

We assess whether recent empirical evidence that Federal Reserve learning caused the Great Inflation is consistent with forecasts published in the Greenbook. If the rise and fall in inflation really was caused by the Federal Reserve learning the Phillips curve then that should be fully reflected in Greenbook forecasts. It is not. The difficulty is that empirical evidence is predicated on the Federal Reserve making forecasts that are much more volatile than those in the Greenbooks. If consistency with Greenbook forecasts is required then evidence that Federal Reserve learning caused the Great Inflation is much weaker. Our results suggest a larger role for other causes than previously thought. * We greatly appreciate Tao Zha making available his C++ routines for Bayesian MCMC estimation. We also thank Knut Anton Mork, Antti Ripatti, and seminar participants at Bank of England, Birkbeck, Cardiff, Durham, Glasgow, Humboldt, Oslo (2nd Workshop on Monetary Policy), Queen's Belfast, Warwick and York for helpful comments and suggestions. Martin Ellison acknowledges support from an ESRC Research Fellowship, "Improving Monetary Policy for the 21st Century" (RES-000-27-0126). The views expressed in this paper do not necessarily reflect those of the ECB.

In this paper we introduce identifying restrictions into a Markov-switching vector autoregression model. We define a separate set of impulse responses for each Markov regime to show how fundamental disturbances affect the variables in the model GHSHQGHQW on the regime. We go to illustrate the use of these regimedependent impulse response functions in a model of the U.S. economy. The regimes we identify come close to the "old" and "new economy" regimes found in recent research. We provide evidence that oil price shocks are much less contractionary and inflationary than they used to be. We show furthermore that the decoupling of the US economic performance from oil price shocks cannot be explained by "good luck" alone, but that structural changes within the US economy have taken place.