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Rational expectations modeling with seasonally adjusted data

  • Christopher A. Sims

In a world where time series show clear seasonal fluctuations, rational agents will take account of those fluctuations in planning their own behavior. Using seasonally adjusted data to model behavior of such agents throws away information and introduces possibly severe bias. Nonetheless it may be true fairly often that rational expectations modeling with seasonally adjusted data, treating the adjusted data as if it were actual data, gives approximately correct results; and naive extensions of standard modeling techniques to seasonally unadjusted data may give worse results than naive use of adjusted data. This paper justifies these claims with examples and detailed arguments.

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Paper provided by Federal Reserve Bank of Minneapolis in its series Discussion Paper / Institute for Empirical Macroeconomics with number 35.

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Date of creation: 1990
Date of revision:
Handle: RePEc:fip:fedmem:35
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