.png){kind=icon}
返回
Modeling time series when some observations are zero
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jeconom.2019.05.003.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sometimes a significant proportion of observations in a time series are zero, but the remaining observations are positive and measured on a continuous scale. We propose a new dynamic model in which the conditional distribution of the observations is constructed by shifting a distribution for non-zero observations to the left and censoring negative values. The key to generalizing the censoring approach to the dynamic case is to have (the logarithm of) the location/scale parameter driven by a filter that depends on the score of the conditional distribution. An exponential link function means that seasonal effects can be incorporated into the model and this is done by means of a cubic spline (which can potentially be time-varying). The model is fitted to daily rainfall in locations in northern Australia and England and compared with a dynamic zero-augmented model. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Censored distributions
Dynamic conditional score model
Generalized beta distribution
Rainfall
Seasonality
Zero augmented model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
5.3K
被引数:
3.0W
机构
引用论文
.png){kind=icon}
查看更多内容