Measuring Business Cycles in Economic Time Series: 154 (Lecture Notes in Statistics, 154) - Softcover

Kaiser, Regina; Maravall, Agustin

Softcover

ISBN 10: 0387951121 ISBN 13: 9780387951126

Publisher: Springer, 2000

Synopsis

lengths, that could not be captured with univariate linear filters. Exam­ ples of research in both directions can be found in Sims (1977), Lahiri and Moore (1991), Stock and Watson (1993), and Hamilton (1994) and (1989). Although the first approach is known to present serious limitations,the new and more sophisticated methods developed in the second approach (most notably, multivariate and nonlinear extensions) are at an early stage, and have proved still unreliable, displaying poor behavior when moving away from the sample period . Despite the fact that business cycle estimation is basic to the conduct of macroeconomic policy and to monitoring of the economy, many decades of attention have shown that formal modeling of economic cycles is a frustrating issue. As Baxter and King (1999) point out, we still face at present the same basic question "as did Burns and Mitchell fifty years ago: how should one isolate the cyclical component of an eco­ nomic time series? In particular, how should one separate business-cycle elements from slowly evolving secular trends, and rapidly varying seasonal or irregular components?" Be that as it may, it is a fact that measuring (in some way) the busi­ ness cycle is an actual pressing need of economists, in particular of those related to the functioning of policy-making agencies and institutions, and of applied macroeconomic research.

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Review

From the reviews:

MATHEMATICAL REVIEWS

"Altogether this book is more on the mathematical side, it is well written following the same idea throughout and contains many exercises which complete the different topics. The text concentrates on the approach of the authors...I enjoyed reading this nicely written book which can certainly be recommended to all mathematically oriented statisticians interested in the subject."

Synopsis

The purpose of the manuscript is to outline and demonstrate problems with the use of the HP filter, and to propose an alternative strategy for inferring cyclical behavior from a time series that features seasonal, trend, cyclical and noise components. The main innovation of the alternative strategy involves augmenting the series in question with forecasts and backcasts obtained from an ARIMA model, and then applying the HP filter to the augmented series. Comparisons presented in the paper using artificial and actual data demonstrate the superiority of the alternative strategy.

Edition details

• Publisher: Springer
• Publication date: 2000
• Language: English
• ISBN 10: 0387951121
• ISBN 13: 9780387951126
• Binding: Paperback
• Number of pages: 198