Forecasting transformed series

  • Authors:

  • C. W. J. Granger;P. Newbold

  • Affiliations:

  • Department of Economics, University of California, San Diego, La Jolla, CA;Department of Economics, Nottingham University, Nottingham, United Kingdom

  • Venue:
  • Essays in econometrics
  • Year:
  • 2001

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Abstract

Suppose that a forecasting model is available for the process Xt but that interest centres on the instantaneous transformation Yt = T(Xt). On the assumption that Xt is Gaussian and stationary, or can be reduced to stationarity by differencing, this paper examines the autocovariance structure of and methods for forecasting the transformed series. The development employs the Hermite polynomial expansion, thus allowing results to be derived for a very general class of instantaneous transformations.