This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Performance Evaluation of the New Connecticut Leading Employment Index Using Lead Profiles and BVAR Models

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Anirvan Banerji (Economic Cycle Research Institute, New York.)
Pami Dua (Delhi School of Economics.)
Stephen M. Miller (University of Nevada, Las Vegas)

Additional information is available for the following registered author(s):

Abstract

Dua and Miller (1996) created leading and coincident employment indexes for the state of Connecticut, following Moore's (1981) work at the national level. The performance of the Dua-Miller indexes following the recession of the early 1990s fell short of expectations. This paper performs two tasks. First, it describes the process of revising the Connecticut Coincident and Leading Employment Indexes. Second, it analyzes the statistical properties and performance of the new indexes by comparing the lead profiles of the new and old indexes as well as their out-of-sample forecasting performance, using the Bayesian Vector Autoregressive (BVAR) method. The new coincident index shows improved performance in dating employment cycle chronologies. The lead profile test demonstrates that superiority in a rigorous, non-parametric statistic fashion. The mixed evidence on the BVAR forecasting experiments illustrates the truth in the Granger and Newbold (1986) caution that leading indexes properly predict cycle turning points and do not necessarily provide accurate forecasts except at turning points, a view that our results support.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help file. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.cdedse.org/pdf/work114.pdf
File Format:
File Function:
Download Restriction: no

Publisher Info
Paper provided by Centre for Development Economics, Delhi School of Economics in its series Working papers with number 114.

Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
Length: 55 pages
Date of creation: Feb 2003
Date of revision:
Handle: RePEc:cde:cdewps:114

Contact details of provider:
Postal: Delhi 110 007
Phone: (011) 27667005
Fax: (011) 27667159
Email:
Web page: http://www.cdedse.org/
More information through EDIRC

Order Information:
Email:
Web: http://www.cdedse.org/

For technical questions regarding this item, or to correct its listing, contact: (Vinayan. K.P).

Related research
Keywords: Business cycles leading and coincident employment indexes turning points BVAR Models

Other versions of this item:

Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models
E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. Richard M. Todd, 1984. "Improving economic forecasting with Bayesian vector autoregression," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall. [Downloadable!]
  2. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January. [Downloadable!] (restricted)
  3. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  4. Pami Dua & Stephen Miller, 1995. "Forecasting and Analyzing Economic Activity with Coincident and Leading Indexes: The Case of Connecticut," Working papers 1995-05, University of Connecticut, Department of Economics. [Downloadable!]
  5. Robert B. Litterman, 1984. "Above-average national growth in 1985 and 1986," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall. [Downloadable!]
  6. Zellner, Arnold & Palm, Franz, 1974. "Time series analysis and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 2(1), pages 17-54, May. [Downloadable!] (restricted)
  7. Christopher A. Sims, 1988. "Bayesian skepticism on unit root econometrics," Discussion Paper / Institute for Empirical Macroeconomics 3, Federal Reserve Bank of Minneapolis. [Downloadable!]
    Other versions:
  8. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-44, January. [Downloadable!] (restricted)
  9. Layton, Allan P & Moore, Geoffrey H, 1989. "Leading Indicators for the Service Sector," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(3), pages 379-86, July.
Full references

Statistics
Access and download statistics

Did you know? You can use convenient plug-ins to search directly IDEAS from your browser.

This page was last updated on 2008-7-22.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.