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.

Natural formation of chloro- and bromoacetone in salt lakes of Western Australia

Western Australia is a semi-/arid region known for saline lakes with a wide range of geochemical parameters (pH 2.5-7.1, Cl- 10-200 g L-1. This study reports on the haloacetones chloro- and bromoacetone in air over 6 salt lake shorelines. Significant emissions of chloroacetone (up to 0.2 µmol m-2 h-1) and bromoacetone (up to 1. 5 µmol m-2 h-1) were detected, and a photochemical box model was employed to evaluate the contribution of their atmospheric formation from the olefinic hydrocarbons propene and methacrolein in the gas phase. The measured concentrations could not explain the photochemical halogenation reaction, indicating a strong hitherto unknown source of haloacetones. Aqueous-phase reactions of haloacetones, investigated in the laboratory using humic acid in concentrated salt solutions, were identified as alternative formation pathway by liquid-phase reactions, acid catalyzed enolization of ketones, and subsequent halogenation. In order to verify this mechanism, we made measurements of the Henry's law constants, rate constants for hydrolysis and nucleophilic exchange with chloride, UV-spectra and quantum yields for the photolysis of bromoacetone and 1,1-dibromoacetone in the aqueous phase. We suggest that heterogeneous processes induced by humic substances in the quasi-liquid layer of the salt crust, particle surfaces and the lake water are the predominating pathways for the formation of the observed haloacetones

The Nature of Financial and Real Business Cycles: The Great Moderation and Banking Sector Pro-Cyclicality

This paper takes a fresh look at the nature of financial and real business cycles in OECD countries using annual data series and shorter quarterly and monthly economic indicators. It first analyses the main characteristics of the cycle, including the length, amplitude, asymmetry and changes of these parameters during expansions and contractions. It then studies the degree of economic and financial cycle synchronisation between OECD countries but also of economic and financial variables within a given country, and gauges the extent to which cycle synchronisation changed over time. Finally, the paper provides some new evidence on the drivers of the great moderation and analyses the banking sector's pro-cyclicality by using aggregate and bank-level data. The main findings show that the amplitude of the real business cycle was becoming smaller during the great moderation, but asset price cycles were becoming more volatile. In part this was linked to developments in the banking sector which tended to accentuate pro-cyclical behaviour

Persistence of dissolved organic matter explained by molecular changes during its passage through soil

Dissolved organic matter affects fundamental biogeochemical processes in the soil such as nutrient cycling and organic matter storage. The current paradigm is that processing of dissolved organic matter converges to recalcitrant molecules (those that resist degradation) of low molecular mass and high molecular diversity through biotic and abiotic processes. Here we demonstrate that the molecular composition and properties of dissolved organic matter continuously change during soil passage and propose that this reflects a continual shifting of its sources. Using ultrahigh-resolution mass spectrometry and nuclear magnetic resonance spectroscopy, we studied the molecular changes of dissolved organic matter from the soil surface to 60 cm depth in 20 temperate grassland communities in soil type Eutric Fluvisol. Applying a semi-quantitative approach, we observed that plant-derived molecules were first broken down into molecules containing a large proportion of low-molecular-mass compounds. These low-molecular-mass compounds became less abundant during soil passage, whereas larger molecules, depleted in plant-related ligno-cellulosic structures, became more abundant. These findings indicate that the small plant-derived molecules were preferentially consumed by microorganisms and transformed into larger microbial-derived molecules. This suggests that dissolved organic matter is not intrinsically recalcitrant but instead persists in soil as a result of simultaneous consumption, transformation and formation

To pay or not to pay? Business owners’ tax morale:testing a neo-institutional framework in a transition environment

In order to understand how the environment influences business owner/managers’ attitudes towards tax morale, we build a theoretical model based on a neo-institutionalist framework. Our model combines three complementary perspectives on institutions—normative, cultural–cognitive and regulatory–instrumental. This enables a broader understanding of factors that influence business owner–managers’ attitudes towards tax evasion. We test the resulting hypotheses using regression analysis on survey data on business owner/managers in Latvia—a transition country, which has undergone massive institutional changes since it was part of the Soviet Union over 25 years ago. We find that legitimacy of the tax authorities and the government (normative dimension), feeling of belonging to the nation (cultural–cognitive dimension) and perceptions of the risk and severity of punishment (regulatory–instrumental dimension) are all associated with higher tax morale for business owners and managers

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