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We propose a new model for the variance between multiple time series, the Regime Switching Dynamic Correlation. We decompose the covariances into correlations and standard deviations and the correlation matrix follows a regime switching model; it is constant within a regime but different across regimes. The transitions between the regimes are governed by a(More)
Thanks are due to Frank Diebold and to Prakash Loungani for insightful comments on the paper. We are grateful to the Deutsche Bank Research for providing the Consensus Forecast dataset used here. The US macroeconomic data were collated from Economic Indicators (Bulletin of the Council of Economic Advisers). The Japanese GDP growth data were kindly provided(More)
Widely publicized reports of fresh MBAs getting multiple job offers with six-figure annual salaries leave a long-lasting general impression about the high quality of selected business schools. While such spectacular achievement in job placement rightly deserves recognition, one should not lose sight of the resources expended in order to accomplish this(More)
The Easterlin hypothesis emphasizes the effect of relative cohort size on fertility. Models based on the Easterlin hypothesis have performed well in explaining time series fertility data, although these results have been for long historical time series and have typically been restricted to single country studies. These models are not adequate to determine(More)
In modeling series with leading or lagging indicators, it is desirable to begin comparing models in terms of time distance. This paper formalizes the concept of time distance in terms of various metrics, and investigates the behaviors of these metrics. It is shown that under some circumstances, time distance metrics indeed perform better in forecasting than(More)