The World Bank's Unified Survey projections : how accurate are they? an ex-post evaluation of US91-US97

Since 1984, the Unified Survey has been the World Bank's principle mechanism for gathering quantitative macroeconomic information from country teams on Bank member countries. After gathering annual data those teams also do most-likely-scenario projections. The author examines the numerical projections of macroeconomic indicators carried out by World Bank country teams for Unified Surveys for fiscal years 1991-97. He studies the accuracy of short-term projects (for the current year, first year, and three years ahead) for 23 countries in the different World Bank regions. He also compares the Unified Survey projections with the International Monetary Fund's (IMF's) projections for its fall World Economic Outlook (WEO). He finds that: 1) The Unified Survey projections are inaccurate when evaluated over the whole period investigated (1990-96). However, their accuracy has improved over time. 2) Improvements are notable in projections for investment, GDP inflation, and government deficit. Projections of external indicators - such as import and export growth - are still substantially inaccurate and should be greatly improved. 3) The Unified Survey projections are as accurate as - or more accurate than - the WEO projections. 4) One cannot characterize the United Survey projections as optimistic. This is the first systematic attempt to evaluate the accuracy of country team macroeconomic projections over time and the first to compare these with the IMF's WEO projections.Poverty Impact Evaluation,Economic Theory&Research,Scientific Research&Science Parks,Public Health Promotion,Environmental Economics&Policies,Economic Theory&Research,Scientific Research&Science Parks,Science Education,Poverty Impact Evaluation,Governance Indicators

On the estimation of a fixed effects model with selective non-response

Economics;Statistical Methods;econometrics

Auxiliary Guided Autoregressive Variational Autoencoders

Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics respectively, suggest hybrid models that encode global image structure into latent variables while autoregressively modeling low level detail. Previous approaches to such hybrid models restrict the capacity of the autoregressive decoder to prevent degenerate models that ignore the latent variables and only rely on autoregressive modeling. Our contribution is a training procedure relying on an auxiliary loss function that controls which information is captured by the latent variables and what is left to the autoregressive decoder. Our approach can leverage arbitrarily powerful autoregressive decoders, achieves state-of-the art quantitative performance among models with latent variables, and generates qualitatively convincing samples.Comment: Published as a conference paper at ECML-PKDD 201

The recurrence of health in urban planning: towards an integration of environmental health aspects

While urban planning and health were initially interlinked, in the twentieth century planning practice slowly moved away from its public health origins. In recent years however there is a growing interest in the health effects of our spatial organization. Although the direct impact of the physical environment on health has decreased – due to better standards of living, sanitary developments, improved housing – environmental quality still deserves our attention. First, the focus has shifted from life expectancy to health expectancy and quality of life. Public health impact no longer predominantly involves clear mortality risks, but rather comprises aspects of human well-being in a broad sense. Several direct impacts, like noise or air pollution, do not immediately kill people, but cause physical or mental disorders on the long term or severely reduce the quality of life of people. Second, the physical environment has many indirect effects on lifestyle and health, for example a reduced physical activity caused by a lack of walkable neighborhoods. A final important reason to justify this research is the aspect of environmental justice. The spatial characteristics responsible for direct and indirect health effects are spatially heterogeneously distributed, causing important differences in health status and healthy life expectancy between various residential neighborhoods. Today a lot of research exists on different health impacts caused by aspects of the physical environment. Most of this research focuses on one single impact (e.g. noise) or one spatial aspect (e.g. a road). An integrated approach, in which all the impacts and aspects are combined, seems to be lacking. However, there is a giant need for a better understanding of this issue, to inform community leaders and spatial planners about which community design and land-use choices are most effective in improving the physical, mental and social well-being of people. In this paper an attempt is made to give an overview of the main environmental characteristics with an effect on people’s health and well-being. The aim is to evaluate the evidence of the existing research output and to explore the relevance for spatial planning. Finally the results are discussed and recommendations for urban planning policy are formulated. Here the aspect of environmental justice comes into view, the right on a healthy living environment for every citizen regardless of social and economic status

Creating a new Ontology: a Modular Approach

Creating a new Ontology: a Modular ApproachComment: in Adrian Paschke, Albert Burger, Andrea Splendiani, M. Scott Marshall, Paolo Romano: Proceedings of the 3rd International Workshop on Semantic Web Applications and Tools for the Life Sciences, Berlin,Germany, December 8-10, 201

Testing for selectivity bias in panel data models

Estimation;Panel Data

Two-step estimation of simultaneous equation panel data models with censored endogenous variables

This paper presents some two-step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias. Our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity/selection bias to include as additional explanatory variables in the primary equation. These are obtained through a decomposition of the reduced form residuals. The panel nature of the data allows adjustment, and testing, for two forms of endogeneity and/or sample selection bias. Furthermore, it incorporates roles for dynamics and state dependence in the reduced form. Finally, we provide an empirical illustration which features our procedure and highlights the ability to test several of the underlying assumptions.Estimation;Panel Data;statistics

The nonresponse bias in the analysis of the determinants of total annual expenditures of households based on panel data

Estimation;Panel Data