Conditional Superior Predictive Ability

Jia Li, Z. Liao, R. Quaedvlieg
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引用次数: 22

Abstract

This article proposes a test for the conditional superior predictive ability (CSPA) of a family of forecasting methods with respect to a benchmark. The test is functional in nature: under the null hypothesis, the benchmark’s conditional expected loss is no more than those of the competitors, uniformly across all conditioning states. By inverting the CSPA tests for a set of benchmarks, we obtain confidence sets for the uniformly most superior method. The econometric inference pertains to testing conditional moment inequalities for time series data with general serial dependence, and we justify its asymptotic validity using a uniform non-parametric inference method based on a new strong approximation theory for mixingales. The usefulness of the method is demonstrated in empirical applications on volatility and inflation forecasting.
条件优越预测能力
本文提出了一种基于基准的预测方法族的条件优越预测能力(CSPA)检验方法。测试本质上是功能性的:在零假设下,基准的条件预期损失不超过竞争对手的条件预期损失,在所有条件作用状态下是一致的。通过对一组基准的CSPA测试进行反转,我们获得了一致最优方法的置信集。对于具有一般序列相关性的时间序列数据,计量经济学推理是检验条件矩不等式的方法,我们利用一种新的基于混合的强逼近理论的一致非参数推理方法证明了它的渐近有效性。在波动性和通货膨胀预测的实证应用中证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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