Pilar Poncela

Real-time monitoring of the economy is based on activity indicators that show regular patterns such as trends, seasonality and business cycles. However, parametric and non-parametric methods for signal extraction produce revisions at the end of the sample, and the arrival of new data makes it difficult to assess the state of the economy. In this paper, we compare two signal extraction procedures: Circulant Singular Spectral Analysis, CiSSA, a non-parametric technique in which we can extract components associated with desired frequencies, and a parametric method based on ARIMA modelling. Through a set of simulations, we show that the magnitude of the revisions produced by CiSSA converges to zero quicker, and it is smaller than that of the alternative procedure.

Wind energy and wind power forecast errors have a direct impact on operational decision problems involved in the integration of this form of energy into the electricity system. As the relationship between wind and the generated power is highly nonlinear and time-varying, and given the increasing number of available forecasting techniques, it is possible to use alternative models to obtain more than one prediction for the same hour and forecast horizon. To increase forecast accuracy, it is possible to combine the different predictions to obtain a better one or to dynamically select the best one in each time period. Hybrid alternatives based on combining a few selected forecasts can be considered when the number of models is large. One of the most popular ways to combine forecasts is to estimate the coefficients of each prediction model based on its past forecast errors. As an alternative, we propose using multivariate reduction techniques and Markov chain models to combine forecasts. The combination is thus not directly based on the forecast errors. We show that the proposed combination strategies based on dimension reduction techniques provide competitive forecasting results in terms of the Mean Square Error.

Commodity prices influence price levels of a broad range of goods and, in the case of some developing economies, production and export activity. Therefore, information about future commodity inflation is useful for central banks, forward-looking policy-makers, and economic agents whose decisions depend on their expectations about it. After 2004, we have witnessed the so-called financialization of the commodity markets, which might induce greater communalities among commodity prices. This paper reports evidence on the relevance of the forecasting content of co-movement after 2004. With the use of large and small scale factor models we find that for the short run, in addition to dynamics, sectoral communality has relevant predictive content. For 12 months ahead, dynamics lose relevance while communality remains relevant.

This paper offers a comprehensive view of international risk sharing and of related policy issues from the perspective of the European Union. The traditional analyses contemplate three risk-sharing channels: the capital markets channel (through cross border portfolio investments), international transfers and the credit markets channel (via savings). Comparative analyses reveal that, on average, about 80% of the shock remains unsmoothed in Europe while only about 18% of the shock is transmitted to consumers within the US. From aggregated figures, there is space for improving, particularly, the cross-border investments channel in Europe. In this sense, the completion of the Banking and Capital Markets Union are expected to boost risk sharing across European member states. We also review new additional issues usually not contemplated by the traditional literature as depreciation, migration and the role of sovereigns and two new additional channels recently considered in the literature: government consumption and the real exchange rate. Finally, we also examine recent analysis related to the geographic distribution of risk sharing.