We estimated a non-Stationary dynamic factor model and used it to generate artificial episodes of disinflation (permanent changes in the mean inflationrate). These datasets were used to test the forecasting abilities of alternative underlying inflation indicators (i.e. measures that capture sustained movements in inflation extracted from information in a disaggregated set of price data).We found that the out of sample forecast errors of the benchmark underlying inflation measures (based on unobserved trend extraction) are more severely affected by disinflation than the alternative simpler methods (based on exclusionor re-weighting approaches). We also show that a non-stationary dynamic factor model may be employed for the extraction of the unobserved trend to be usedas an underlying inflation measure.

The article examines the disciplinary preferences of medical and psychology writers of research articles (RAs) in the use of epistemic lexical verbs (ELVs), regarding their frequency, prominence, distribution across the RA sections, and recurrent phraseology. The results show that disciplinary affiliation affects these phenomena, as more ELVs are found in psychology than in medicine. Both groups prefer speculative judgements and quotative evidence and most frequently use ELVs in Discussions. Yet, psychology authors are more balanced in their preferences and rely on a wider selection of frequent ELVs which are often combined with self-mention. Medical authors are more inclined towards deductive ELVs. Disciplinary differences are also observed in the choice of the specific ELVs, their frequency distributions and phraseology in the distinct RA sections.