Christian Bontemps, Moment-based tests under parameter uncertainty, The Review of Economics and Statistics, 2018, forthcoming.


This paper considers moment-based tests applied to estimated quantities. We propose a general class of transforms of moments to handle the parameter uncertainty problem. The construction requires only a linear correction that can be implemented in-sample and remains valid for some extended families of non-smooth moments. We reemphasize the attractiveness of working with robust moments, which lead to testing procedures that do not depend on the estimator. Furthermore, no correction is needed when considering the implied test statistic in the out-of-sample case. We apply our methodology to various examples with an emphasis on the backtesting of value-at-risk forecasts.


moment-based tests; parameter uncertainty; out-of-sample; discrete distributions; value-at-risk; backtesting;

JEL codes

  • C12: Hypothesis Testing: General

Research partnership

Scor Chair