A game-theoretic version of Oakes' example for randomized forecasting

TL;DR

This paper explores the limits of randomized forecasting within a game-theoretic framework, demonstrating conditions under which forecasts can pass statistical tests and providing a generalized example showing failure of deterministic methods.

Contribution

It extends existing results by providing a lower bound and a generalized example for randomized forecasting, highlighting the impact of forecast accuracy constraints.

Findings

Randomized forecasts can pass countable statistical tests with unrestricted accuracy.

A lower bound shows limitations when forecast accuracy is fixed.

A generalized game-theoretic example demonstrates failure of deterministic forecasting methods.

Abstract

Using the game-theoretic framework for probability, Vovk and Shafer. have shown that it is always possible, using randomization, to make sequential probability forecasts that pass any countable set of well-behaved statistical tests. This result generalizes work by other authors, who consider only tests of calbration. We complement this result with a lower bound. We show that Vovk and Shafer's result is valid only when the forecasts are computed with unrestrictedly increasing degree of accuracy. When some level of discreteness is fixed, we present a game-theoretic generalization of Oakes' example for randomized forecasting that is a test failing any given method of deferministic forecasting; originally, this example was presented for deterministic calibration.