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Dating Business Cycle Turning Points
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Year: 2005 Publisher: Cambridge, Mass. National Bureau of Economic Research

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Dating business cycle turning points
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Year: 2005 Publisher: Cambridge, Mass. NBER

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Financial aggregation and index number theory.
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ISBN: 9789814293099 Year: 2010 Publisher: Singapore World scientific

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Financial aggregation and index number theory
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ISBN: 1283148331 9786613148339 9814293105 9781283148337 9789814293105 Year: 2011 Publisher: Hackensack, N.J. : World Scientific Pub.,

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The book surveys modern literature on financial aggregation and index number theory, with special emphasis on the contributions of the book's two coauthors. In addition to a systematic survey chapter unifying the rest of the book, this publication contains reprints of published articles that are central to the survey chapter. ""Financial Aggregation and Index Number Theory"" provides a reference work for financial data researchers and users of central bank data, placing emphasis on possible improvements in such data from use of the microeconomic index number and aggregation theory.


Book
Dating Business Cycle Turning Points
Authors: --- ---
Year: 2005 Publisher: Cambridge, Mass. National Bureau of Economic Research

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This paper discusses formal quantitative algorithms that can be used to identify business cycle turning points. An intuitive, graphical derivation of these algorithms is presented along with a description of how they can be implemented making very minimal distributional assumptions. We also provide the intuition and detailed description of these algorithms for both simple parametric univariate inference as well as latent-variable multiple-indicator inference using a state-space Markov-switching approach. We illustrate the promise of this approach by reconstructing the inferences that would have been generated if parameters had to be estimated and inferences drawn based on data as they were originally released at each historical date. Waiting until one extra quarter of GDP growth is reported or one extra month of the monthly indicators released before making a call of a business cycle turning point helps reduce the risk of misclassification. We introduce two new measures for dating business cycle turning points, which we call the "quarterly real-time GDP-based recession probability index" and the "monthly real-time multiple-indicator recession probability index" that incorporate these principles. Both indexes perform quite well in simulation with real-time data bases. We also discuss some of the potential complicating factors one might want to consider for such an analysis, such as the reduced volatility of output growth rates since 1984 and the changing cyclical behavior of employment. Although such refinements can improve the inference, we nevertheless find that the simpler specifications perform very well historically and may be more robust for recognizing future business cycle turning points of unknown character.

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The Future of Oil : Geology Versus Technology
Authors: --- --- --- --- --- et al.
ISBN: 1475534604 1475567405 Year: 2012 Publisher: Washington, D.C. : International Monetary Fund,

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We discuss and reconcile two diametrically opposed views concerning the future of world oil production and prices. The geological view expects that physical constraints will dominate the future evolution of oil output and prices. It is supported by the fact that world oil production has plateaued since 2005 despite historically high prices, and that spare capacity has been near historic lows. The technological view of oil expects that higher oil prices must eventually have a decisive effect on oil output, by encouraging technological solutions. It is supported by the fact that high prices have, since 2003, led to upward revisions in production forecasts based on a purely geological view. We present a nonlinear econometric model of the world oil market that encompasses both views. The model performs far better than existing empirical models in forecasting oil prices and oil output out of sample. Its point forecast is for a near doubling of the real price of oil over the coming decade. The error bands are wide, and reflect sharply differing judgments on ultimately recoverable reserves, and on future price elasticities of oil demand and supply.

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