A new method to "clean up" ultra high-frequency data

In the applied econometrics, the availability of ultra high-frequency databases is having an important impact on the research market microstructure theory. The ultra high-frequency databases contain detailed reports of all the financial market activity information which is available. However, ultra high-frequency databases cannot be directly used. On one hand recording mistakes can be present, on the other hand missing information has to be inferred from the available data. In this paper, we propose a simple method in order to clean up the ultra high-frequency data from possible errors and we examine the method efficacy when we analyze data by using an autoregressive conditional duration (ACD) model.