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  • MPI Bildungsforschung  (4)
  • Topographie des Terrors und DZ
  • Chamorro, Andres  (4)
  • 1
    UID:
    gbv_1666260363
    Format: 1 Online-Ressource (circa 57 Seiten) , Illustrationen
    Series Statement: Policy research working paper 8757
    Content: This paper introduces a Spatial Vector Autoregressive Moving Average (SVARMA) model in which multiple cross-sectional time series are modeled as multivariate, possibly fat-tailed, spatial autoregressive ARMA processes. The estimation requires specifying the cross-sectional spillover channels through spatial weights matrices. the paper explores a kernel method to estimate the network topology based on similarities in the data. It discusses the model and estimation, focusing on a penalized Maximum Likelihood criterion. The empirical performance of the estimator is explored in a simulation study. The model is used to study a spatial time series of pollution and household expenditure data in Indonesia. The analysis finds that the new model improves in terms of implied density, and better neutralizes residual correlations than the VARMA, using fewer parameters. The results suggest that growth in household expenditures precedes pollution reduction, particularly after the expenditures of poorer households increase; that increasing pollution is followed by reduced growth in expenditures, particularly reducing the growth of poorer households; and that there are significant spillovers from bottom-up growth in expenditures. The paper does not find evidence for top-down growth spillovers. Feedback between the identified mechanisms may contribute to pollution-poverty traps and the results imply that pollution damages are economically significant
    Additional Edition: Erscheint auch als Druck-Ausgabe Andree, Bo Pieter Johannes Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks Washington, D.C : The World Bank, 2019
    Language: English
    Keywords: Graue Literatur
    URL: Volltext  (lizenzpflichtig)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    gbv_1666260142
    Format: 1 Online-Ressource (circa 51 Seiten) , Illustrationen
    Series Statement: Policy research working paper 8756
    Content: This paper revisits the issue of environment and development raised in the 1992 World Development Report, with new analysis tools and data. The paper discusses inference and interpretation in a machine learning framework. The results suggest that production gradually favors conserving the earth's resources as gross domestic product increases, but increased efficiency alone is not sufficient to offset the effects of growth in scale. Instead, structural change in the economy shapes environmental outcomes across GDP. The analysis finds that average development is associated with an inverted 'U'-shape in deforestation, pollution, and carbon intensities. Per capita emissions follow a 'J'-curve. Specifically, poverty reduction occurs alongside degrading local environments and higher income growth poses a global burden through carbon. Local economic structure further determines the shape, amplitude, and location of tipping points of the Environmental Kuznets Curve. The models are used to extrapolate environmental output to 2030. The daunting implications of continued development are a reminder that immediate and sustained global efforts are required to mitigate forest loss, improve air quality, and shift the global economy to a 2 Degree pathway
    Additional Edition: Erscheint auch als Druck-Ausgabe Andree, Bo Pieter Johannes Environment and Development: Penalized Non-Parametric Inference of Global Trends in Deforestation, Pollution and Carbon Washington, D.C : The World Bank, 2019
    Language: English
    Keywords: Graue Literatur
    URL: Volltext  (lizenzpflichtig)
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Online Resource
    Online Resource
    [Washington, DC, USA] : World Bank Group, Fragility, Conflict and Violence Global Theme & Development Economics Vice Presidency
    UID:
    gbv_1743503679
    Format: 1 Online-Ressource (circa 35 Seiten) , Illustrationen
    Series Statement: Policy research working paper 9412
    Content: Globally, more than 130 million people are estimated to be in food crisis. These humanitarian disasters are associated with severe impacts on livelihoods that can reverse years of development gains. The existing outlooks of crisis-affected populations rely on expert assessment of evidence and are limited in their temporal frequency and ability to look beyond several months. This paper presents a statistical foresting approach to predict the outbreak of food crises with sufficient lead time for preventive action. Different use cases are explored related to possible alternative targeting policies and the levels at which finance is typically unlocked. The results indicate that, particularly at longer forecasting horizons, the statistical predictions compare favorably to expert-based outlooks. The paper concludes that statistical models demonstrate good ability to detect future outbreaks of food crises and that using statistical forecasting approaches may help increase lead time for action
    Additional Edition: Erscheint auch als Druck-Ausgabe Andree, Bo Pieter Johannes Predicting Food Crises Washington, D.C : The World Bank, 2020
    Language: English
    Keywords: Graue Literatur
    Author information: Kraay, Aart
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    Online Resource
    Online Resource
    [Washington, DC, USA] : World Bank Group, Fragility, Conflict and Violence Global Theme
    UID:
    gbv_174350389X
    Format: 1 Online-Ressource (circa 30 Seiten) , Illustrationen
    Series Statement: Policy research working paper 9413
    Content: Recent advances in food insecurity classification have made analytical approaches to predict and inform response to food crises possible. This paper develops a predictive, statistical framework to identify drivers of food insecurity risk with simulation capabilities for scenario analyses, risk assessment and forecasting purposes. It utilizes a panel vector-autoregression to model food insecurity distributions of 15 Sub-Saharan African countries between October 2009 and February 2019. Statistical variable selection methods are employed to identify the most important agronomic, weather, conflict and economic variables. The paper finds that food insecurity dynamics are asymmetric and past-dependent, with low insecurity states more likely to transition to high insecurity states than vice versa. Conflict variables are more relevant for dynamics in highly critical stages, while agronomic and weather variables are more important for less critical states. Food prices are predictive for all cases. A Bayesian extension is introduced to incorporate expert opinions through the use of priors, which lead to significant improvements in model performance
    Additional Edition: Erscheint auch als Druck-Ausgabe Wang, Dieter Stochastic Modeling of Food Insecurity Washington, D.C : The World Bank, 2020
    Language: English
    Keywords: Graue Literatur
    Library Location Call Number Volume/Issue/Year Availability
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