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The reproducible properties of correct forecasts

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Author Info
Alvaro Sandroni ()
Abstract

Each period, one outcome out of finitely many possibilities is observed. Each period, a forecaster announces some probability for the future outcomes based on the available data. An outsider wants to know if the forecaster has some knowledge of the data generating process. Let a test be an arbitrary function from sequences of forecasts and outcomes to {0,1}. When the test returns a 0 the test is said to reject the forecasts based on the outcome sequence. When the test resturns a 1 the test is said to not reject the forecasts based on the outcome sequence. Consider any test that does not reject the truth, i.e. it does not reject when the announced forecasts are the conditional probabilities of the data generating process. Based on Fan’s (1953) Minimax theorem, I show that it is possible to produce forecasts that will not be rejected on any sequence of outcomes. Copyright Springer-Verlag 2003

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Publisher Info
Article provided by Springer in its journal International Journal of Games Theory.

Volume (Year): 32 (2003)
Issue (Month): 1 (December)
Pages: 151-159
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Handle: RePEc:spr:jogath:v:32:y:2003:i:1:p:151-159

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Related research
Keywords: Forecasting; Testing; Calibration; Minimax theorem;

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  1. Alvaro Sandroni & Wojciech Olszewski, 2008. "Manipulability of Future-Independent Tests," PIER Working Paper Archive 08-014, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania. [Downloadable!]
    Other versions:
  2. Feinberg, Yossi & Dekel, Eddie, 2004. "A True Expert Knows which Question Should Be Asked," Research Papers 1856, Stanford University, Graduate School of Business. [Downloadable!]
    Other versions:
  3. Alvaro Sandroni & Wojciech Olszewski, 2008. "Strategic Manipulation of Empirical Tests," PIER Working Paper Archive 08-015, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania. [Downloadable!]
    Other versions:
  4. Alvaro Sandroni & Wojciech Olszewski, 2008. "Falsifiability," PIER Working Paper Archive 08-016, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania. [Downloadable!]
  5. Colin Stewart, 2009. "Nonmanipulable Bayesian Testing," Working Papers tecipa-360, University of Toronto, Department of Economics. [Downloadable!]
  6. Glen Weyl, 2009. "A Simple Theory of Scientific Learning," Levine's Working Paper Archive 814577000000000067, David K. Levine. [Downloadable!]
  7. Feinberg, Yossi & Stewart, Colin, 2007. "Testing Multiple Forecasters," Research Papers 1957, Stanford University, Graduate School of Business. [Downloadable!]
    Other versions:
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