Testing serial dependence or cross dependence for time series with underreporting

In practice, it is common for collected data to be underreported, which is particularly prevalent in fields such as social sciences, ecology and epidemiology. Drawing inferences from such data using conventional statistical methods can lead to incorrect conclusions. In this paper, we study tests for serial or cross dependence in time series data that are subject to underreporting. We introduce new test statistics, develop corresponding group-of-blocks bootstrap techniques, and establish their consistency. The methods are shown to be efficient by simulation and are used to identify key factors responsible for the spread of dengue fever and the occurrence of cardiovascular disease.