Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.

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Econometric Analysis of Panel Data

世界経済・社会統計 = World development indicators

计量经济分析 = Econometric analysis

Efforts to understand and mitigate the health effects of particulate matter (PM) air pollution have a rich and interesting history. This review focuses on six substantial lines of research that have been pursued since 1997 that have helped elucidate our understanding about the effects of PM on human health. There has been substantial progress in the evaluation of PM health effects at different time-scales of exposure and in the exploration of the shape of the concentration-response function. There has also been emerging evidence of PM-related cardiovascular health effects and growing knowledge regarding interconnected general pathophysiological pathways that link PM exposure with cardiopulmonary morbidity and mortality. Despite important gaps in scientific knowledge and continued reasons for some skepticism, a comprehensive evaluation of the research findings provides persuasive evidence that exposure to fine particulate air pollution has adverse effects on cardiopulmonary health. Although much of this research has been motivated by environmental public health policy, these results have important scientific, medical, and public health implications that are broader than debates over legally mandated air quality standards.

/pdf/health-effects-of-fine-particulate-air-pollution-lines-that-s831sp8jap.pdf

Health effects of fine particulate air pollution: lines that connect

This paper reviews the concept of adaptation of human communities to global changes, especially climate change, in the context of adaptive capacity and vulnerability. It focuses on scholarship that contributes to practical implementation of adaptations at the community scale. In numerous social science fields, adaptations are considered as responses to risks associated with the interaction of environmental hazards and human vulnerability or adaptive capacity. In the climate change field, adaptation analyses have been undertaken for several distinct purposes. Impact assessments assume adaptations to estimate damages to longer term climate scenarios with and without adjustments. Evaluations of specified adaptation options aim to identify preferred measures. Vulnerability indices seek to provide relative vulnerability scores for countries, regions or communities. The main purpose of participatory vulnerability assessments is to identify adaptation strategies that are feasible and practical in communities. The distinctive features of adaptation analyses with this purpose are outlined, and common elements of this approach are described. Practical adaptation initiatives tend to focus on risks that are already problematic, climate is considered together with other environmental and social stresses, and adaptations are mostly integrated or mainstreamed into other resource management, disaster preparedness and sustainable development programs. r 2006 Elsevier Ltd. All rights reserved.

/pdf/adaptation-adaptive-capacity-and-vulnerability-3txg5bpzf9.pdf

Adaptation, adaptive capacity and vulnerability

This paper examines African livestock management across Agro-Ecological Zones (AEZs) to learn how they would adapt to climate change in the coming century. We analyze farm level decisions to own livestock and to choose a primary livestock species using logit models with and without country fixed effects or AEZ fixed effects. With a hot dry scenario, the results indicate that livestock ownership will increase slightly across all of Africa, but especially in West Africa and high elevation AEZs. Dairy cattle will decrease in semi-arid regions, sheep will increase in lowlands, and rearing chickens will increase at high elevations. On the other hand, if climate becomes wetter, livestock ownership will fall dramatically in lowlands and high elevation moist AEZs. Beef cattle will increase and sheep will fall in dry AEZs, dairy cattle will fall precipitously and goats will rise in moist AEZs, and chickens will increase at high elevations but fall at mid elevations. Therefore, adaptation measures should be tailored to a specific AEZ.

/pdf/adapting-to-climate-change-mosaically-an-analysis-of-african-427ngtkies.pdf

Adapting to Climate Change Mosaically: An Analysis of African Livestock Management by Agro-Ecological Zones

Although storing more carbon in forests should be part of an efficient mitigation program, it is unclear how to create effective incentives to make this happen. The literature largely has focused on giving landowners direct incentives to store carbon. This paper explores an alternative mechanism to increase forest carbon sequestration by creating a market for wood bioenergy. By raising the value of wood, the program encourages landowners to convert vast amounts of land to forest, which incidentally increases forest carbon. By providing an indirect subsidy on woody biomass, governments can give even more incentive to reward this carbon sequestration.

https://www.jstor.org/stable/10.1086/676033

Using Markets for Woody Biomass Energy to Sequester Carbon in Forests

A financial analysis of rain forest silviculture in southwestern Sri Lanka

This article examines methods for measuring climate adaptation and discusses the empirical evidence. The primary methods for measuring climate adaptation are cross-sectional analysis, simul...

Measuring Climate Adaptation: Methods and Evidence

One of the banes of farming is the frequent complete loss of crops due to adverse weather conditions or pests. In this paper, we explore what causes catastrophic crop failures. The study demonstrates that 39% of the variation in expected crop failure rates across the United States can be explained by soils and climate. The analysis shows that precipitation, soils, and especially temperature all explain average crop failure rates. Surprisingly, the analysis did not suggest that annual warming would increase crop failure rates. However, decreases in annual precipitation or increases in interannual or diurnal variation would all be harmful to crops.

Robert Mendelsohn

Papers

Adapting to Climate Change Mosaically: An Analysis of African Livestock Management by Agro-Ecological Zones

Using Markets for Woody Biomass Energy to Sequester Carbon in Forests

A financial analysis of rain forest silviculture in southwestern Sri Lanka

Measuring Climate Adaptation: Methods and Evidence

What Causes Crop Failure