António Rua

Short-Term Forecasting of GDP Using Large Monthly Datasets: A Pseudo Real-Time Forecast Evaluation Exercise

This paper evaluates different models for the short-term forecasting of real GDP growth in ten selected European countries and the euro area as a whole. Purely quarterly models are compared with models designed to exploit early releases of monthly indicators for the nowcast and forecast of quarterly GDP growth. Amongst the latter, we consider small bridge equations and forecast equations in which the bridging between monthly and quarterly data is achieved through a regression on factors extracted from large monthly datasets. The forecasting exercise is performed in a simulated real-time context, which takes account of publication lags in the individual series. In general, we find that models that exploit monthly information outperform models that use purely quarterly data and, amongst the former, factor models perform best. JEL Classification: E37, C53.

Tracking the Business Cycle of the Euro Area: A Multivariate Model-Based Bandpass Filter

This article proposes a multivariate bandpass filter based on the trend plus cycle decomposition model. The underlying multivariate dynamic factor model relies on specific formulations for trend and cycle components and produces smooth business cycle indicators with bandpass filter properties. Furthermore, cycle shifts for individual time series are incorporated as part of the multivariate model and estimated simultaneously with the remaining parameters. The inclusion of leading, coincident, and lagging variables for the measurement of the business cycle is therefore possible without a prior analysis of lead–lag relationships between economic variables. This method also permits the inclusion of time series recorded with mixed frequencies. For example, quarterly and monthly time series can be considered simultaneously without ad hoc interpolations. The multivariate approach leads to a business cycle indicator that is less subject to revisions than those produced by univariate filters. The reduction of revisions is a key feature in real-time assessment of the economy. Finally, the proposed method computes a growth indicator as a byproduct. The new approach of tracking business cycle and growth indicators is illustrated in detail for the Euro area. The analysis is based on nine key economic time series.

Money growth and inflation in the euro area: a time-frequency view

This article provides new insights on the relationship between money growth and inflation in the euro area over the last 40 years. This highly relevant link for the European Central Bank monetary policy strategy is assessed using wavelet analysis. The findings indicate a stronger link between inflation and money growth at low frequencies over the whole sample period. At the typical business cycle frequency range the link is only present until the beginning of the 1980s. Moreover, there seems to be a recent deterioration of the leading properties of money growth with respect to inflation in the euro area.

Relationship between rain composition in Spain and its sources

Factor analysis is used to evaluate relationships among inorganic ions to provide a qualitative determination of the kind of ion sources Spanish wet deposition. This procedure identifies four factors: sea, soil, aerosol, and industrial. The factor that reproduces a larger fraction of the variance is the soil or soil/aerosol and the factor reproducing a lower fraction of the variance is the industrial. The conditional probability function (CPF) [Ashbaugh et al., 1985] is used to identify possible sources for each ion, in geographical terms. The Mediterranean region is the most important source of inorganic ions and the British Isles the single source, although intermediate, of acid rain.

Surface ozone in Spain

Abstract This article presents an analysis of the surface ozone in five Spanish rural stations with the aim of studying the diurnal and seasonal variations of ozone concentration, the mechanisms that control it and its geographical sources. Results show diurnal cycles in three of the five considered stations, as well as spring maxima in four and summer maxima in three of them. The Conditional Probability Functions (CPFs) are used to identify the geographical sources of O3 . The most important source areas are those with strong NOx emissions and far away the stations. Another objective of the article is to essay a method to identifify the temporal and spatial domain of the mechanisms to produce O3. This method suggests that the photochemical production of O3 from focal NOx seems to be the main mechanism to produce O3 when the length of the trajectory of the air mass is short. When this length increases, NOx emission gains importance as the main mechanism controlling O3 concentration.

Worldwide synchronization since the nineteenth century: a wavelet-based view

Resorting to wavelet analysis, a novel measure is used to assess synchronization of economic activity across a large set of countries. As it has long been acknowledged in the literature that synchronization can vary over time and may depend on the type of fluctuation, the use of a wavelet-based measure of synchronization becomes particularly useful as it can capture both time and frequency varying features within a unified framework. Considering the period since 1870s up to 2011 for a set of 23 countries, it is found that worldwide synchronization has increased over the last decades and it has attained an unprecedented level during the Great Recession.

Tracking Growth and the Business Cycle: A Stochastic Common Cycle Model for the Euro Area

This paper proposes a new model-based method to obtain a coincident indicator for the business cycle. A dynamic factor model with trend components and a common cycle component is considered which can be estimated using standard maximum likelihood methods. The multivariate unobserved components model includes a stationary higher order cycle. Also higher order trends can be part of the analysis. These generalisationslead to a business cycle that is similar to a band-pass one. Furthermore, cycle shifts for individual time series are incorporated within the model and estimated simultaneously with the remaining parameters. This feature permits the use of leading, coincident and lagging variables to obtain thebusiness cycle coincident indicator without prior analysis of their lead-lag relationship. Besides the business cycle indicator, the model-based approach also allows to get a growth rate indicator. In the empirical analysis for the Euro area, both indicators are obtained based on nine key economic timeseries including gross domestic product, industrial production,unemployment, confidence indicators and interest rate spread. This analysis contrasts sharply with earlier multivariate approaches. In particular, our more parsimonious approach leads to a growth rate indicator for the Euro area that is similar to the one of EuroCOIN. The latter is based on a more involvedapproach by any standard and uses hundreds of time series from individual countries belonging to the Euro area.

Sources of SO2, SO4 2-, NOx, and NO3- in the Air of Four Spanish Remote Stations

In this study we have analyzed the sources of SO2, SO42-, Nox, 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.

Dynamic Factor Models with Jagged Edge Panel Data: Taking on Board the Dynamics of the Idiosyncratic Components

As macroeconomic data are released with different delays, one has to handle unbalanced panel data sets with missing values at the end of the sample period when estimating dynamic factor models. We propose an EM algorithm which copes with such data sets while accounting for autoregressive common factors and allowing for serial correlation in the idiosyncratic components. Based on Monte Carlo simulations, we find that taking on board the dynamics of the idiosyncratic components improves significantly the accuracy of the estimation of both the missing values and the common factors at the end of the sample period.

Relationship between air pollutants emission patterns and concentrations

Emission patterns of NOx and S‐compounds are analyzed to study their influence on the concentrations of SO2, NOx, sulphate and nitrate in the air. Air mass trajectories, emission inventories and cluster analysis are used to define the emission patterns. The scheme that characterized most of the days is defined by low emissions from 48 hours until 18 hours before the measurements and it produces average concentrations. High concentrations are due to emission peaks. The time between these emission peaks and the measurement determines the importance of the emission peak on the concentration.