15,391 research outputs found

    Autocorrelation and masked heterogeneity in panel data models estimated by maximum likelihood

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    In a panel data model with random effects, when autocorrelation in the error is considered, (Gaussian) maximum likelihood estimation produces a dramatically large number of corner solutions: the variance of the random effect appears (incorrectly) to be zero, and a larger autocorrelation is (incorrectly) assigned to the idiosyncratic component. Thus heterogeneity could (incorrectly) be lost in applications to panel data with customarily available time dimension, even in a correctly specified model. The problem occurs in linear as well as nonlinear models. This paper aims at pointing out how serious this problem can be (largely neglected by the panel data literature). A set of Monte Carlo experiments is conducted to highlight its relevance, and we explain this unpleasant effect showing that, along a direction, the expected log-likelihood is nearly flat. We also provide two examples of applications with corner solutions.Panel data, autocorrelation, random effects, maximum likelihood, expected log-likelihood

    Application of Remote Sensing to the Chesapeake Bay Region. Volume 2: Proceedings

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    A conference was held on the application of remote sensing to the Chesapeake Bay region. Copies of the papers, resource contributions, panel discussions, and reports of the working groups are presented

    Unintended effects of urbanization in China: Land use spillovers and soil carbon loss

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    This paper uses a national-level geographic information system database on land use, weather conditions, land quality, soil organic carbon (SOC), topographic features, and economic variables to analyze the major drivers of land use change and the resulting impact on soil carbon storage in China. The framework developed in this study includes two main components. One is a spatial panel multinomial logit land use model that takes into account the spatial and temporal dependence of land use choices explicitly. The other is a statistical causal evaluation model that estimates the effect of land use change on SOC density. Results indicate that local economic growth, as measured by county-level gross domestic product, was a major cause of urban development and grassland conversions. Rapid expansion of road networks, promoted by massive public investment, increased the conversion of forests, grassland, and unused land to crop production and urban development. Urbanization had significant secondary ripple effects in terms of both indirect land use change and soil carbon loss. Some of the soil carbon loss may be irreversible, at least in the short run.Land use, propensity score-matching, road density, soil organic carbon, spatial panel,

    The determinants of the recent interregional migration flows in Italy: A panel data analysis

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    The present study investigates the determinants of interregional migration flows in Italy in the light of the upsurge occurred in 1996, after two decades of decreasing internal migration rates. We apply the fixed effect vector decomposition estimator (FEVD) on a gravity model using bilateral migration flows for the period 1996-2005 and show that it improves the estimates with respect to the traditional panel data estimators. We find that omitting distance and in presence of rarely time invariant covariates (e.g., population and income) the standard panel data models significantly bias the estimates. The overall economic level and the probability to find a job (proxied by per capita GDP and unemployment rate) appear to be the key variables whose changes are able to push flows of migrants away from their regions and to direct them to “better off” destinations. We find that migrants leaving the regions in the Centre-North respond differently to the push and pull forces with respect to southern migrants. We then estimate a dynamic model and find evidence for the presence of social networks which in our model take place between each pair of regions.Interregional migration, gravity model, panel data, FEVD.

    Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change

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    This Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) has been jointly coordinated by Working Groups I (WGI) and II (WGII) of the Intergovernmental Panel on Climate Change (IPCC). The report focuses on the relationship between climate change and extreme weather and climate events, the impacts of such events, and the strategies to manage the associated risks. The IPCC was jointly established in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP), in particular to assess in a comprehensive, objective, and transparent manner all the relevant scientific, technical, and socioeconomic information to contribute in understanding the scientific basis of risk of human-induced climate change, the potential impacts, and the adaptation and mitigation options. Beginning in 1990, the IPCC has produced a series of Assessment Reports, Special Reports, Technical Papers, methodologies, and other key documents which have since become the standard references for policymakers and scientists.This Special Report, in particular, contributes to frame the challenge of dealing with extreme weather and climate events as an issue in decisionmaking under uncertainty, analyzing response in the context of risk management. The report consists of nine chapters, covering risk management; observed and projected changes in extreme weather and climate events; exposure and vulnerability to as well as losses resulting from such events; adaptation options from the local to the international scale; the role of sustainable development in modulating risks; and insights from specific case studies

    Motor Carrier Panel

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    Technical Change and Total Factor Productivity Growth for Chinese Provinces: A Panel Data Analysis

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    We present in this paper the panel econometrics estimation approach of measuring the technical change and total factor productivity (TFP) growth of 30 Chinese provinces during the period of 1993 to 2003. The random effects model with heteroscedastic variances has been used for the estimation of the translog production functions. Two alternative formulations of technical change measured by the single time trend and the general index approach are used. Based on the measures of technical change, estimates of TFP growth could be obtained and its determinants were examined using regression analysis. The parametric TFP growth measure is compared with the non-parametric Solow residual. TFP has recorded positive growth for all provinces during the sample period. Regional breakdown shows that the eastern and central regions have higher average TFP growth when compared with the western region. Foreign direct investment (FDI) and information and communication technology (ICT) investment are found to be significant factors contributing to the TFP difference. While these two factors are found to have significant influence on TFP, their influence on production is relatively small compared to traditional inputs of production.technical change; TFP growth; provinces; China; ICT; FDI; infrastructure
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