Business cycle clocks: Time to get circular

Assessing the momentum of the business cycle is of utmost importance for policymakers and private agents. In this respect, the use of business cycle clocks has gained prominence among national and international institutions to depict the current stage of the business cycle. Drawing on circular statistics, we propose a novel approach to business cycle clocks in a data-rich environment. The method is applied to the main euro area countries resorting to a large data set covering the last three decades. We document the usefulness of the circular business cycle clock to capture the business cycle stage, including peaks and troughs, with the findings being supported by the cross-country evidence.

The Daily Economic Indicator: tracking economic activity daily during the lockdown

The SARS-CoV-2 outbreak made clear the urgent need to depart from traditional statistics, typically released with a lag and available at a relatively low frequency. This led to unparalleled efforts to put forward high-frequency indicators to track economic developments timely. By resorting to non-traditional data sources, we propose a novel daily economic indicator to track economic activity in Portugal. It corresponds to the latent variable of a set of daily series within a factor model framework. We find a sudden and sharp drop in economic activity in mid-March 2020, when the lockdown of several activities was declared due to the COVID-19 pandemic. Since in this approach we address the complexities of high-frequency data without further smoothing, we are able to identify sudden changes of economic activity in a timely and daily manner in contrast with other approaches.

Sources of SO2, SO42-, NOx, and NO3- in the Air of Four Spanish Remote Stations

In this study we have analyzed the sources of SO₂, SO₄^2-, No_x, and NO₃^- in the air of four remote Spanish stations belonging to the European Monitoring and Evaluation Programme (EMEP) network. Information about trajectories has been used together with the conditional probability functions (CPFs).¹ The most remarkable result is that the Mediterranean area is the main source of these pollutants in the air of the Spanish EMEP stations. Northern Africa and Central Europe are also important sources while the Atlantic Ocean and British Islands are, in general terms, low sources of these pollutants. The role of the Iberian Peninsula as a source of pollutants in one of the stations, Logrono, is analyzed with more details using smaller regions to define CPFs.