Forecasting GDP at the regional level with many predictors

In this paper, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden- Württemberg) and Eastern Germany. We overcome the problem of a ’data-poor environment’ at the sub-national level by complementing various regional indicators with more than 200 national and international indicators. We calculate single– indicator, multi–indicator, pooled and factor forecasts in a pseudo real–time setting. Our results show that we can significantly increase forecast accuracy compared to an autoregressive benchmark model, both for short and long term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP

Marshall or Jacobs? Answers to an unsuitable question from an interaction model

This paper investigates whether localization economies as brought forward by Marshall(1890) or urbanization economies as mentioned by Jacobs (1970) are more decisive forregional gross value added per capita. Our novel approach is to explicitly allow forinterdependencies between these two theories and to take into account that the initiallevels of specialization and diversification might play a role. We therefore deploy amodel with interaction terms and find that these two theories are not mutually exclusivein most of our sectors. In addition, the empirical results show that the initial levels ofspecialization and diversification do matter as well.Localization and urbanization economies, interaction models, regional gross valued added

Particle Filter Design Using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environments

Sequential Monte Carlo methods have been recently proposed to deal with the problem of acoustic source localisation and tracking using an array of microphones. Previous implementations make use of the basic bootstrap particle filter, whereas a more general approach involves the concept of importance sampling. In this paper, we develop a new particle filter for acoustic source localisation using importance sampling, and compare its tracking ability with that of a bootstrap algorithm proposed previously in the literature. Experimental results obtained with simulated reverberant samples and real audio recordings demonstrate that the new algorithm is more suitable for practical applications due to its reinitialisation capabilities, despite showing a slightly lower average tracking accuracy. A real-time implementation of the algorithm also shows that the proposed particle filter can reliably track a person talking in real reverberant rooms.This paper was performed while Eric A. Lehmann was working with National ICT Australia. National ICT Australia is funded by the Australian Government’s Department of Communications, Information Technology, and the Arts, the Australian Research Council, through Backing Australia’s Ability, and the ICT Centre of Excellence programs

Survey-based indicators vs. hard data: What improves export forecasts in Europe?

We evaluate whether survey-based indicators produce lower forecast errors for export growth than indicators obtained from hard data such as price and cost competitiveness measures. Our pseudo out-of-sample analyzes and forecast encompassing tests reveal that survey-based indicators outperform the benchmark model as well as the indicators from hard data for most of our 20 European states and the aggregates EA-18 and EU-28. The most accurate forecasts are on average produced by the confidence indicator in the manufacturing sector, the economic sentiment indicator and the production expectations. However, large country differences in the forecast accuracy of survey-based indicators emerge. These differences are mainly explained with country-specific export compositions. A larger share in raw material or oil exports worsens the accuracy of soft indicators. The accuracy of soft indicators improves if countries have a larger share in exports of machinery goods. For hard indicators, we find only weak evidence for the export composition to explain differences in forecast accuracy

Survey-based indicators vs. hard data: What improves export forecasts in Europe?

We evaluate whether survey-based indicators produce lower forecast errors for export growth than indicators obtained from hard data such as price and cost competitiveness measures. Our pseudo out-of-sample analyzes and forecast encompassing tests reveal that survey-based indicators outperform the benchmark model as well as the indicators from hard data for most of our 20 European states and the aggregates EA-18 and EU-28. The most accurate forecasts are on average produced by the confidence indicator in the manufacturing sector, the economic sentiment indicator and the production expectations. However, large country differences in the forecast accuracy of survey-based indicators emerge. These differences are mainly explained with country-specific export compositions. A larger share in raw material or oil exports worsens the accuracy of soft indicators. The accuracy of soft indicators improves if countries have a larger share in exports of machinery goods. For hard indicators, we find only weak evidence for the export composition to explain differences in forecast accuracy

Survey-based indicators vs. hard data: What improves export forecasts in Europe?

In this study, we evaluate whether survey-based indicators produce lower forecast errorsfor export growth than indicators obtained from hard data such as price and costcompetitiveness measures. Our pseudo out-of-sample analyses and forecastencompassingtests reveal that survey-based indicators outperform the benchmarkmodel as well as the indicators from hard data for most of the twenty European statesfocused on in our study and the aggregates EA-18 and EU-28. The most accurate forecastsare on average produced by the confidence indicator in the manufacturing sector,the economic sentiment indicator and the production expectations. However, largecountry differences in the forecast accuracy of survey-based indicators emerge. Thesedifferences are mainly explained by country-specific export compositions. A largershare in raw material or oil exports worsens the accuracy of soft indicators. The accuracyof soft indicators improves if countries have a larger share in exports of machinerygoods. For hard indicators, we find only weak evidence for the export composition toexplain differences in forecast accuracy

Mitología sudamericana. X : La astronomía de los tobas [segunda parte]

Fil: Lehmann-Nitsche, Robert. División Antropología; Facultad de Ciencias Naturales y Museo; Universidad Nacional de La Plat

Catálogo de las antigüedades de la provincia de Jujuy observadas en el Museo de La Plata

Fil: Lehmann-Nitsche, Robert. División Antropología; Facultad de Ciencias Naturales y Museo; Universidad Nacional de La Plat

Los morteros de Capilla del Monte : contribución a la arqueología argentina

Fil: Lehmann-Nitsche, Robert. División Antropología; Facultad de Ciencias Naturales y Museo; Universidad Nacional de La Plat

Strategies to mitigate greenhouse gas and nitrogen emissions in Swiss agriculture: the application of an integrated sector model

Environmental impacts of agricultural production, such as greenhouse gas (GHG) and nitrogen emissions, are of major concern for scientists and policy makers throughout the world. Global agricultural activities account for about 60% of nitrous oxide and about 50% of methane emissions. From a global perspective, methane and nitrous oxide constitute crucial GHGs. They contribute substantially to climate change due to their high potential for effecting global warming compared to carbon dioxide. Emissions of these gases depend on the extent of agricultural production and applied technologies. Therefore, analysis of potential mitigation opportunities is challenging and requires an integrated approach in order to link agricultural economic perspectives to environmental aspects. In view of this, a mathematical programming model has been developed which enables assessment of cost-effective strategies for mitigating GHG and nitrogen emissions in the agricultural sector in Switzerland. This model is applied to improve understanding of the agricultural sector and its behavior with changing conditions in technology and policy. The presented recursive-dynamic model mimics the structure and inter- dependencies of Swiss agriculture and links that framework to core sources of GHG and nitrogen emissions. Calculated results for evaluation and application indicate that employed flexibility constraints provide a feasible approach to sufficiently validate the described model. Recursive-dynamic elements additionally enable adequate modeling of both an endogenous development of livestock dynamics and investments in buildings and machinery, also taking sunk costs into account. The presented findings reveal that the specified model approach is suitable to accurately estimate agricultural structure, GHG and nitrogen emissions within a tolerable range. The model performance can therefore be described as sufficiently robust and satisfactory. Thus, the model described here appropriately models strategies for GHG and nitrogen abatement in Swiss agriculture. The results indicate that there are limits to the ability of Swiss agriculture to contribute substantially to the mitigation of GHG and nitrogen emissions. There is only a limited level of mitigation available through technical approaches, and these approaches have high cost.resource use, environmental economics, greenhouse gas emission, nitrogen emission, integrated modeling