The National Bank of Belgium, Research Department’s new business survey indicator

The business survey indicator is one of the most valuable statistics that the Bank publishes every month. Its reputation is due to the reliability it has demonstrated over several decades in reflecting the pattern of economic activity in the country and in the euro area every month. The indicator is compiled on the basis of the responses to the monthly business survey that the Bank has arranged with enterprises in Belgium since 1954. Almost twenty years after the last methodological revision of the indicator in 1990, the Bank decided that it was now desirable to review its method of calculation again. This article presents the key characteristics of the business survey indicator, its practical applications and the new method of calculation applied since April 2009. This methodological revision gradually became necessary owing to the extension of the survey in 1994 to business-related services, the results of which were not included in the general business survey indicator until this methodological change. The old business survey indicator had also exhibited some undesirable short-term fluctuations. The methodological changes have been kept to a minimum and only concern the calculation of the synthetic curves, with an amended selection of questions that are included in the synthetic curves for each industry and by incorporating the business-related services curve into the overall synthetic business indicator. These changes aim to strengthen the correlation between the indicator and GDP growth, to reduce the undesirable short-term volatility and to maintain its early response.business cycle, business survey, leading indicator, correlation, GDP

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.Bridge models, Dynamic factor models, real-time data flow model

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.Bridge models ; Dynamic factor models ; real-time data flow.

Macroecology meets macroevolution: evolutionary niche dynamics in the seaweed <i>Halimeda</i>

Aim Because of their broad distribution in geographical and ecological dimensions, seaweeds (marine macroalgae) offer great potential as models for marine biogeographical inquiry and exploration of the interface between macroecology and macroevolution. This study aims to characterize evolutionary niche dynamics in the common green seaweed genus Halimeda, use the observed insights to gain understanding of the biogeographical history of the genus and predict habitats that can be targeted for the discovery of species of special biogeographical interest.Location Tropical and subtropical coastal waters.Methods The evolutionary history of the genus is characterized using molecular phylogenetics and relaxed molecular clock analysis. Niche modelling is carried out with maximum entropy techniques and uses macroecological data derived from global satellite imagery. Evolutionary niche dynamics are inferred through application of ancestral character state estimation.Results A nearly comprehensive molecular phylogeny of the genus was inferred from a six-locus dataset. Macroecological niche models showed that species distribution ranges are considerably smaller than their potential ranges. We show strong phylogenetic signal in various macroecological niche features.Main conclusions The evolution of Halimeda is characterized by conservatism for tropical, nutrient-depleted habitats, yet one section of the genus managed to invade colder habitats multiple times independently. Niche models indicate that the restricted geographical ranges of Halimeda species are not due to habitat unsuitability, strengthening the case for dispersal limitation. Niche models identified hotspots of habitat suitability of Caribbean species in the eastern Pacific Ocean. We propose that these hotspots be targeted for discovery of new species separated from their Caribbean siblings since the Pliocene rise of the Central American Isthmus

Methodological Note

AbstractThe second part of the book will present the results of an empirical study of the experience of Ecuadorian irregular migrants in the cities of Amsterdam and Madrid. While the general overview of the methodological conceptualization that is the backbone of the whole research work has already been outlined in the introduction, in this brief chapter the focus will be centred on the specific methodological issues concerning the empirical study

Holocene dynamics of the Southern Hemisphere westerly winds and possible links to CO2 outgassing

The Southern Hemisphere westerly winds (SHW) play an important role in regulating the capacity of the Southern Ocean carbon sink. They modulate upwelling of carbon-rich deep water and, with sea ice, determine the ocean surface area available for air–sea gas exchange. Some models indicate that the current strengthening and poleward shift of these winds will weaken the carbon sink. If correct, centennial- to millennial-scale reconstructions of the SHW intensity should be linked with past changes in atmospheric CO2, temperature and sea ice. Here we present a 12,300-year reconstruction of wind strength based on three independent proxies that track inputs of sea-salt aerosols and minerogenic particles accumulating in lake sediments on sub-Antarctic Macquarie Island. Between about 12.1 thousand years ago (ka) and 11.2 ka, and since about 7 ka, the wind intensities were above their long-term mean and corresponded with increasing atmospheric CO2. Conversely, from about 11.2 to 7.2 ka, the wind intensities were below their long-term mean and corresponded with decreasing atmospheric CO2. These observations are consistent with model inferences of enhanced SHW contributing to the long-term outgassing of CO2 from the Southern Ocean

Ecuadorian Migration in Amsterdam and Madrid: The Structural Contexts

AbstractThe scope of this chapter is to outline the main characteristics of the two contexts that were the scenario of the phenomenon that is the object of this book. The analysis will centre on those structural features of the cities of Amsterdam and Madrid and, more in general, of the Netherlands and Spain, that may have had an influence on the experience of Ecuadorian irregular migrants

The Study of Irregular Migration

AbstractThe study of irregular migration as a specific social phenomenon took off during the 70s in the US. Since then, the academic interest has continually grown and spread, first to Europe and, in the last years, to other regions worldwide. This interest can certainly be related to the increasing attention paid to the study of migrations more in general (Castles & Miller, 1993). The trend can be linked to those broad and complex social and economic changes, often subsumed under the concept of globalization. The specific focus on irregular migration, though gaining momentum throughout the 1980s, reached preeminent attention in the 1990s. On both sides of the Atlantic, the explosion of the so-called "migration crisis" (Zolberg & Benda, 2001) and the emergence of irregular migration as a widespread social fact raised the attention of public opinion and academics alike. Moreover, in recent years, what seemed at first to be an issue concerning only the high-income regions of the planet, now involves also medium and low-income ones, making irregular migration a truly global structural phenomenon (Cvajner & Sciortino, 2010a; Düvell, 2006)

Irregular Migration Theories

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Short-term forecasting of GDP using large monthly datasets - A pseudo real-time forecast evaluation exercise. NBB Working Papers. No. 133, 17 June 2008

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