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The Predictive Space, or, If x predicts y, what does y tell us about x?

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Author Info

  • Donald Robertson

    (University of Cambridge)

  • Stephen Wright

    (Department of Economics, Mathematics & Statistics, Birkbeck)

Abstract

A predictive regression for y(t) and a time series representation of the predictors, x(t), together imply a univariate reduced form for y(t). In this paper we work backwards, and ask: if we observe y(t), what do its univariate properties tell us about any x(t) in the "predictive space" consistent with those properties? We provide a mathematical characterisation of the predictive space and certain of its derived properties. We derive both a lower and an upper bound for the R^2 for any predictive regression for y(t). We also show that for some empirically relevant univariate properties of y(t), the entire predictive space can be very tightly constrained. We illustrate using Stock and Watson's (2007) univariate representation of inflation.

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File URL: http://www.bbk.ac.uk/ems/research/wp/2012/PDFs/BWPEF1210.pdf
File Function: First version, 2012
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Bibliographic Info

Paper provided by Birkbeck, Department of Economics, Mathematics & Statistics in its series Birkbeck Working Papers in Economics and Finance with number 1210.

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Date of creation: Apr 2012
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Handle: RePEc:bbk:bbkefp:1210

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  1. Jean Boivin & Marc Giannoni, 2006. "DSGE Models in a Data-Rich Environment," NBER Working Papers 12772, National Bureau of Economic Research, Inc.
  2. Campbell, John Y & Mankiw, N Gregory, 1987. "Are Output Fluctuations Transitory?," The Quarterly Journal of Economics, MIT Press, vol. 102(4), pages 857-80, November.
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Cited by:
  1. Andrea Monticini & Francesco Ravazzolo, 2011. "Forecasting the intraday market price of money," Working Paper 2011/06, Norges Bank.

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