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Conditional Superior Predictive Ability
The Review of Economic Studies ( IF 7.833 ) Pub Date : 2021-06-30 , DOI: 10.1093/restud/rdab039
Jia Li 1 , Zhipeng Liao 2 , Rogier Quaedvlieg 3
Affiliation  

Abstract
This paper 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 nonparametric 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.


中文翻译:

有条件的超强预测能力

摘要
本文针对基准提出了对一系列预测方法的条件优越预测能力 (CSPA) 的测试。该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预期损失,在所有条件状态下都是一致的。通过反转一组基准的 CSPA 测试,我们获得了一致最优方法的置信度集。计量经济学推理与测试具有一般序列相关性的时间序列数据的条件矩不等式有关,我们使用基于新的混合量强逼近理论的统一非参数推理方法证明其渐近有效性。该方法的实用性在波动率和通货膨胀预测的实证应用中得到了证明。
更新日期:2021-07-01
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