There has been mounting evidence that the inflation process has been changing. Inflation is now much lower and much more stable around the globe. And its sensitivity to measures of economic slack and increases in input costs appears to have declined. Probably the most widely supported explanation for this phenomenon is that monetary policy has been much more effective. There is no doubt in our mind that this explanation goes a long way towards explaining the better inflation performance we have observed. In this paper, however, we begin to explore a complementary, rather than alternative, explanation. We argue that prevailing models of inflation are too "country-centric", in the sense that they fail to take sufficient account of the role of global factors in influencing the inflation process. The relevance of a more "globe-centric" approach is likely to have increased as the process of integration of the world economy has gathered momentum, a process commonly referred to as "globalisation". In a large cross-section of countries, we find some rather striking prima facie evidence that this has indeed been the case. In particular, proxies for global economic slack add considerable explanatory power to traditional benchmark inflation rate equations, even allowing for the influence of traditional indicators of external influences on domestic inflation, such as import and oil prices. Moreover, the role of such global factors has been growing over time, especially since the 1990s. And in a number of cases, global factors appear to have supplanted the role of domestic measures of economic slack.

This article examines differences in expansionary and contractionary phases of the business cycle. By extending the nonlinear Markov-switching estimation method of Hamilton to incorporate time-varying probabilities of transitions between the phases, the marginal benefits oi the timevarying transition probability Markov-switching model are highlighted. Using this technique, ! document the high correlation between the evolution of the phases inferred from the model and traditional reference cycles for monthly output data. Many of the economic variables rhat determine the time-varying probabilities help to predict turning points. The predictive power of standard leading indicators is evaluated and compared.

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