摘要翻译：
我们发展了一个新的连续时间渐近框架来推断给定预测模型的预测能力是否随时间保持稳定。本文从经济预测者的角度对预测不稳定性进行了正式定义，并强调了预测不稳定性的持续时间与稳定期无关。我们的方法适用于包含低频和高频宏观经济和金融变量的预测环境。当观测之间的采样间隔缩小到零时，预测损失序列被一个具有一定路径性质的连续时间随机过程（即Ito半鞅）所逼近。基于损失序列的连续时限对应的局部性质，建立了一个假设检验问题。空分布遵循一个极值分布。在很好地控制统计量大小的同时，我们的检验统计量类在样本中预测失败的位置上具有均匀功率。检验统计量的设计具有抵抗一般形式的贪得无厌的能力，并对常见形式的非平稳性如异方差和序列相关具有鲁棒性。相对于现有的方法，功率的提高是显著的，尤其是当不稳定性持续时间短且发生在样品尾部时。
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英文标题：
《Tests for Forecast Instability and Forecast Failure under a Continuous
  Record Asymptotic Framework》
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作者：
Alessandro Casini
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最新提交年份：
2018
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  We develop a novel continuous-time asymptotic framework for inference on whether the predictive ability of a given forecast model remains stable over time. We formally define forecast instability from the economic forecaster's perspective and highlight that the time duration of the instability bears no relationship with stable period. Our approach is applicable in forecasting environment involving low-frequency as well as high-frequency macroeconomic and financial variables. As the sampling interval between observations shrinks to zero the sequence of forecast losses is approximated by a continuous-time stochastic process (i.e., an Ito semimartingale) possessing certain pathwise properties. We build an hypotheses testing problem based on the local properties of the continuous-time limit counterpart of the sequence of losses. The null distribution follows an extreme value distribution. While controlling the statistical size well, our class of test statistics feature uniform power over the location of the forecast failure in the sample. The test statistics are designed to have power against general form of insatiability and are robust to common forms of non-stationarity such as heteroskedasticty and serial correlation. The gains in power are substantial relative to extant methods, especially when the instability is short-lasting and when occurs toward the tail of the sample.
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PDF链接：
https://arxiv.org/pdf/1803.10883