Practitioners do not always use research findings, sometimes because the research is not always conducted in a manner relevant to real-world practice. This survey seeks to close the gap between research and practice on short-term forecasting in real time. Towards this end, we review the most...

To perform real-time business cycle inferences and forecasts of GDP growth rates in the Euro area, we use an extension of the Markov-switching dynamic factor models that accounts for the specificities of the day to day monitoring of economic developments such as ragged edges, mixed frequencies...

We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov-switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully non- linear multivariate specification (one-step...

We extend the Markov-switching dynamic factor model to account for some of the specificities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as ragged edges and mixed frequencies. We examine the theoretical benefits of this extension and corroborate the...

We develop a dynamic factor model to compute short term forecasts of the Spanish GDP growth in real time. With this model, we compute a business cycle index which works well as an indicator of the business cycle conditions in Spain. To examine its real time forecasting accuracy, we use real-time...

We show that an extension of the Markov-switching dynamic factor models that accounts for the specificities of the day to day monitoring of economic developments such as ragged edges, mixed frequencies and data revisions is a good tool to forecast the Euro area recessions in real time. We...

We set out a model to compute short-term forecasts of the euro area GDP growth in real-time. To allow for forecast evaluation, we construct a real-time data set that changes for each vintage date and includes the exact information that was available at the time of each forecast. With this data...