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.

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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.

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Adaptation, adaptive capacity and vulnerability

Adaptations are changes in behavior and capital motivated by climate change. Economic theory suggests that adaptations are efficient (desirable) only if their benefit exceeds their cost. Private adaptations are likely to be efficient because the benefits and cost accrue to the decision maker. With some important exceptions, private adaptation is likely to be done efficiently by markets. Public adaptations, however, benefit many people. Markets are not likely to make efficient public adaptations because they cannot coordinate payments from multiple consumers. Governments need to be responsible for public adaptations. However, government must think carefully about being efficient. Empirically, little is known about what precise changes in behavior are efficient, where such changes should take place and when they should take place. The empirical literature has largely focused on how actors have adapted to the current range of climates across the earth. From these studies, researchers are extrapolating what changes would make sense in the future as climate changes. The results suggest that adaptations are local in nature and therefore look like patchwork adjustments across space. They depend on the current local climate, how it changes and the various local conditions. Although public adaptations in health and conservation look promising, there are virtually no economic analyses of their potential. Overall, adaptation can be very effective at reducing damages, for example, by building sea walls to protect from inundation, and seizing new opportunities, for example, by growing in crops in places that were previously too cold. Research must now focus on making the practical steps that turn that potential into reality.

The economics of adaptation to climate change in developing countries

This paper provides an economic perspective of adaptation to climate change. The paper specifically examines the role of markets and government in efficient adaptation responses. For adaptations to be efficient, the benefits from following adaptations must exceed the costs. For private market goods, market actors will follow this principle in their own interest. For public goods, governments must take on this responsibility. Governments must also be careful to design institutions that encourage efficiency or they could inadvertently increase the damages from climate change. Finally, although in a few cases actors must anticipate climate changes far into the future, generally it is best to learn and then act with respect to adaptation.

The Role of Markets and Governments in Helping Society Adapt to a Changing Climate

Marginal abatement cost (MAC) CO2; Metal contamination; Subsidy; Energy crops; Adapted gross income (AGI); Phytoremediation

Phytoremediation, a sustainable remediation technology? II: Economic assessment of CO2 abatement through the use of phytoremediation crops for renewable energy production.

Global climate change is expected to cause sea level rise, which will have major effects on Singapore because it is a small, low-lying island state. With the high degree of urbanization and industrialization on the island, land is scarce and very valuable. Examining three sea level rise scenarios for the next century, we explore whether Singapore should defend their coast or allow it to be inundated. Across ten coastal sites representing all market land in Singapore, we found that protection was the lowest cost strategy. The annual cost of protecting the coasts of Singapore will rise over time as the sea level rises and will range from 0.3 to 5.7 million US$ by 2050 to 0.9 to 16.8 million US$ by 2100. The present value of these costs ranges from 0.17 to 3.08 million US$ depending on the sea level rise scenario.

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Robert Mendelsohn

Papers

The economics of adaptation to climate change in developing countries

The Role of Markets and Governments in Helping Society Adapt to a Changing Climate

Phytoremediation, a sustainable remediation technology? II: Economic assessment of CO2 abatement through the use of phytoremediation crops for renewable energy production.

The impact of sea level rise on Singapore

An empirical analysis of air pollution dose-response curves