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Forecasting inflation and output: comparing data-rich models with simple rules

  • William T. Gavin
  • Kevin L. Kliesen

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.

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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2006-054.

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Date of creation: 2006
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Publication status: Published in Federal Reserve Bank of St. Louis Review, May/June 2008, 90(3, Part 1), pp. 175-92
Handle: RePEc:fip:fedlwp:2006-054
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