Forecasting inflation and output
Subtitle: comparing data-rich models with simple rules
By: by William T. Gavin and Kevin L. Kliesen
Contributors: Kliesen, Kevin L.; Federal Reserve Bank of St. Louis
Series: Working paper -- 2006-054A; Working paper (Federal Reserve Bank of St. Louis : Online) -- 2006-054A.
Publisher: Federal Reserve Bank of St. Louis
Published: 2006
Place of publication: St. Louis, Mo.]
Language: eng
Format: [electronic resource] :
Description: "Decision makers, both public and private, use forecasts of economic growth and inflation to make plans and implement policies.  In many situations, reasonably good forecasts can be made with simple rules of thumb that are extrapolations of a single data series.  In principle, information about other economic indicators should be useful in forecasting a particular series like inflation or output.  Including too many variables makes a model unwieldy and not including enough can increase forecast error.  A key problem is deciding which other series to include.  Recently, studies have shown that Dynamic Factor Models (DFMs) may provide a general solution to this problem.  The key is that these models use a large data set to extract a few common factors (thus, the term 'data-rich').  This paper uses a monthly DFM model to forecast inflation and output growth at horizons of 3, 12 and 24 months ahead.  These forecasts are then compared to simple forecasting rules"--Federal Reserve Bank of St. Louis web site.
Notes: Title from PDF file as viewed on 10/2/2006.

Includes bibliographical references.

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