Identifying narratives in texts is a challenging task, as not only narrative elements such as the factors and events have to be identified but their semantic relation has to be explained as well. Despite this complexity, an effective technique to extract narratives from texts can have a great impact on how we view political and economical developments. By analyzing narratives, one can get a better understanding of how such narratives spread across the media landscape and change our world views as a result. In this paper, we take a closer look into a recently proposed definition of a collective economic narrative that is characterized by containing a cause-effect relation which is used to explain a situation for a given world view. For the extraction of such collective economic narratives, we propose a novel pipeline that improves the RELATIO-method for statement detection. By filtering the corpus for causal articles and connecting statements by detecting causality between them, our augmented RELATIO approach adapts well to identify more complex narratives following our definition. Our approach also improves the consistency of the RELATIO-method by augmenting it with additional pre- and post-processing steps that enhance the statement detection by the means of Coreference Resolution and automatically filters out unwanted noise in the form of uninterpretable statements. We illustrate the performance of this new pipeline in detecting collective economic narratives by analyzing a Financial Times data set that we filtered for economic and inflation-related terms as well as causal indicators.