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Book
Exploration of Trend-Cycle Decomposition Methodologies in Simulated Data
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Year: 2020 Publisher: National Bureau of Economic Research

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Book
International financial management
Authors: ---
ISBN: 9781107111820 110711182X Year: 2020 Publisher: Cambridge Cambridge University Press

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This new and fully updated edition of International Financial Management blends theory, data analysis, examples and practical case situations to equip students and business leaders with the analytical tools they need to make informed financial decisions and manage the risks that businesses face in today's competitive global environment. Combining theory and practice, the authors offer the reader a multitude of real-world examples and case studies, emphasising fundamental concepts, principles and analytical theories to enable students to understand not only what to do when confronted with an international financial decision, but why that choice is the correct one. Features include: real data analysis - all fully updated for the third edition; extended cases illustrating practical application of theory; point-counterpoints offering insight into contentious issues; concept boxes that explore and illustrate key concepts; and end-of-chapter questions. Suitable for M.B.A and advanced undergraduate business students taking a course in international financial management or international finance. --


Book
An Exploration of Trend-Cycle Decomposition Methodologies in Simulated Data
Authors: ---
Year: 2020 Publisher: Cambridge, Mass. National Bureau of Economic Research

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This paper uses simulations to explore the properties of the HP filter of Hodrick and Prescott (1997), the BK filter of Baxter and King (1999), and the H filter of Hamilton (2018) that are designed to decompose a univariate time series into trend and cyclical components. Each simulated time series approximates the natural logarithms of U.S. Real GDP, and they are a random walk, an ARIMA model, two unobserved components models, and models with slowly changing nonstationary stochastic trends and definitive cyclical components. In basic time series, the H filter dominates the HP and BK filters in more closely characterizing the underlying framework, but in more complex models, the reverse is true.

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