Book/Printed Material Estimating turning points using large data sets

Title

• Estimating turning points using large data sets

Summary

• "Dating business cycles entails ascertaining economy-wide turning points. Broadly speaking, there are two approaches in the literature. The first approach, which dates to Burns and Mitchell (1946), is to identify turning points individually in a large number of series, then to look for a common date that could be called an aggregate turning point. The second approach, which has been the focus of more recent academic and applied work, is to look for turning points in a few, or just one, aggregate. This paper examines these two approaches to the identification of turning points. We provide a nonparametric definition of a turning point (an estimand) based on a population of time series. This leads to estimators of turning points, sampling distributions, and standard errors for turning points based on a sample of series. We consider both simple random sampling and stratified sampling. The empirical part of the analysis is based on a data set of 270 disaggregated monthly real economic time series for the U.S., 1959-2010"--National Bureau of Economic Research web site.

Names

• Stock, James H.
• Watson, Mark W.
• National Bureau of Economic Research

Created / Published

• Cambridge, MA : National Bureau of Economic Research, c2010.

Notes

• -  Title from PDF file as viewed on 3/24/2011.
• -  Includes bibliographical references.
• -  Also available in print.
• -  Mode of access: World Wide Web.
• -  System requirements: Adobe Acrobat Reader.

Call Number/Physical Location

• HB1

Digital Id

• https://hdl.loc.gov/loc.gdc/gdcebookspublic.2011655782
• http://www.nber.org/papers/w16532 External

Library of Congress Control Number

• 2011655782

Access Advisory

• Unrestricted online access

Online Format

• image
• pdf

LCCN Permalink

• https://lccn.loc.gov/2011655782

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