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AuthorKaiser, Regina. author
TitleMeasuring Business Cycles in Economic Time Series [electronic resource] / by Regina Kaiser, Agustรญn Maravall
ImprintNew York, NY : Springer New York : Imprint: Springer, 2001
Connect tohttp://dx.doi.org/10.1007/978-1-4613-0129-5
Descript VIII, 190 p. 1 illus. in color. online resource

SUMMARY

lengths, that could not be captured with univariate linear filters. Examยญ 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 ecoยญ 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 busiยญ 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


CONTENT

1 Introduction and Brief Summary -- 2 A Brief Review of Applied Time Series Analysis -- 2.1 Some Basic Concepts -- 2.2 Stochastic Processes and Stationarity -- 2.3 Differencing -- 2.4 Linear Stationary Process, Wold Representation. and Auto-correlation Function -- 2.5 The Spectrum -- 2.6 Linear Filters and Their Squared Gain -- 3 ARIMA Models and Signal Extraction -- 3.1 ARIMA Models -- 3.2 Modeling Strategy, Diagnostics and Inference -- 3.3 Preadjustment -- 3.4 Unobserved Components and Signal Extraction -- 3.5 ARIMA-Model-Based Decomposition of a Time Series -- 3.6 Short-Term and Long-Term Trends -- 4 Detrending and the Hodrick-Prescott Filter -- 4.1 The Hodrick-Prescott Filter: Equivalent Representations -- 4.2 Basic Characteristics of the Hodrick-Prescott Filter -- 4.3 Some Criticisms and Discussion of the Hodrick-Prescott Filter -- 4.4 The Hodrick-Prescott Filter as a Wiener-Kolmogorov Filter -- 5 Some Basic Limitations of the Hodrick-Prescott Filter -- 5.1 Endpoint Estimation and Revisions -- 5.2 Spurious Results -- 5.3 Noisy Cyclical Signal -- 6 Improving the Hodrick-Prescott Filter -- 6.1 Reducing Revisions -- 6.2 Improving the Cyclical Signal -- 7 Hodrick-Prescott Filtering Within a Model-Based Approach -- 7.1 A Simple Model-Based Algorithm -- 7.2 A Complete Model-Based Method; Spuriousness Reconsidered -- 7.3 Some Comments on Model-Based Diagnostics and Inference -- 7.4 MMSE Estimation of the Cycle: A Paradox -- References -- Author Index


Statistics Econometrics Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Econometrics



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