Forecasting Inflation and Output: Comparing Data-Rich Models with Simple Rules (ICPSR 22684)

Published: Jun 10, 2008

Principal Investigator(s):
William T. Gavin, Federal Reserve Bank of St. Louis; Kevin L. Kliesen, Federal Reserve Bank of St. Louis

https://doi.org/10.3886/ICPSR22684.v1

Version V1

There has been a resurgence of interest in dynamic factor models for use by policy advisors. Dynamic factor methods can be used to incorporate a wide range of economic information when forecasting or measuring economic shocks. This article introduces dynamic factor models that underlie the data-rich methods and also tests whether the data-rich models can help a benchmark autoregressive model forecast alternative measures of inflation and real economic activity at horizons of 3, 12, and 24 months ahead. The authors find that, over the past decade, the data-rich models significantly improve the forecasts for a variety of real output and inflation indicators. For all the series that they examine, the authors find that the data-rich models become more useful when forecasting over longer horizons. The exception is the unemployment rate, where the principal components provide significant forecasting information at all horizons.

Gavin, William T., and Kliesen, Kevin L. Forecasting Inflation and Output: Comparing Data-Rich Models with Simple Rules. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2008-06-10. https://doi.org/10.3886/ICPSR22684.v1

Export Citation:

  • RIS (generic format for RefWorks, EndNote, etc.)
  • EndNote

Federal Reserve Bank of St. Louis. Research Division

(1) A zipped package contains program files and a Microsoft Excel file, which contains data, tables, and corresponding figures. (2) These data are part of ICPSR's Publication-Related Archive and are distributed exactly as they arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigators if further information is desired.

2008-06-10

2008-06-10

Notes

  • These data are flagged as replication datasets and are distributed exactly as they arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.

  • The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.

ICPSR logo

This study is provided by ICPSR. ICPSR provides leadership and training in data access, curation, and methods of analysis for a diverse and expanding social science research community.