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.

/pdf/econometric-analysis-of-panel-data-46ld72a5p1.pdf

Econometric Analysis of Panel Data

世界経済・社会統計 = World development indicators

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Examining the correlation between trade and income cannot identify the direction of causation between the two. Countries’ geographic characteristics, however, have important effects on trade, and are plausibly uncorrelated with other determinants of income. This paper therefore constructs measures of the geographic component of countries’ trade, and uses those measures to obtain instrumental variables estimates of the effect of trade on income. The results provide no evidence that ordinary least-squares estimates overstate the effects of trade. Further, they suggest that trade has a quantitatively large and robust, though only moderately statistically significant, positive effect on income. (JEL F43, 040)

/pdf/does-trade-cause-growth-532nt8hsja.pdf

Does Trade Cause Growth

We investigate whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997), with negative results. We then investigate the evolution of income inequality over the same period and its correlation with growth. The dominating feature is inequality convergence across countries. This convergence has been significantly faster amongst developed countries. Growth does not appear to influence the evolution of inequality over time. Outline

https://www.econstor.eu/bitstream/10419/81822/1/02-28.pdf

Economic growth and income inequality

Good Countries or Good Projects

Social welfare functions that assign weights to individuals based on their income levels can be used to document the relative importance of growth and inequality changes for changes in social welfare. In a large panel of industrial and developing countries over the past 40 years, most of the cross-country and over-time variation in changes in social welfare is due to changes in average incomes. In contrast, the changes in inequality observed during this period are on average much smaller than changes in average incomes, are uncorrelated with changes in average incomes, and have contributed relatively little to changes in social welfare.

Growth, Inequality, and Social Welfare: Cross-Country Evidence

The authors use data from more than 6,000 World Bank projects evaluated between 1983 and 2009 to investigate macro and micro correlates of project outcomes. They find that country-level"macro"measures of the quality of policies and institutions are very strongly correlated with project outcomes, confirming the importance of country-level performance for the effective use of aid resources. However, a striking feature of the data is that the success of individual development projects varies much more within countries than it does between countries. The authors assemble a large set of project-level"micro"correlates of project outcomes in an effort to explain some of this within-country variation. They find that measures of project size, the extent of project supervision, and evaluation lags are all significantly correlated with project outcomes, as are early-warning indicators that flag problematic projects during the implementation stage. They also find that measures of World Bank project task manager quality matter significantly for the ultimate outcome of projects. They discuss the implications of these findings for donor policies aimed at aid effectiveness.

https://papers.ssrn.com/sol3/Delivery.cfm/5646.pdf?abstractid=1832162&type=2

Good Countries or Good Projects? Macro and Micro Correlates of World Bank Project Performance

Growth is pro-poor if the poverty measure of interest falls. According to this definition there are three potential sources of pro-poor growth: (1) a high rate of growth of average incomes; (2) a high sensitivity of poverty to growth in average incomes; and (3) a poverty-reducing pattern of growth in relative incomes. The author empirically decomposes changes in poverty in a large sample of developing countries during the 1980s and 1990s into these three components. In the medium to long run, most of the variation in changes in poverty can be attributed to growth in average incomes, suggesting that policies and institutions that promote broad-based growth should be central to the pro-poor growth agenda. Most of the remainder of the variation in poverty is due to poverty-reducing patterns of growth in relative incomes, rather than differences in the sensitivity of poverty to growth in average incomes. Cross-country evidence provides relatively little guidance as to the policies and institutions that promote these other sources of pro-poor growth.

When is Growth Pro-Poor? Cross-Country Evidence

What are the effects of increased trade in goods and services on the trade balance? We study the effects of reducing transport costs in a Ricardian model with complete asset markets. Trade integration has three effects on the structure of the economy: a reduction in the home bias in consumption, an increase in the degree of international competition in goods markets, and a reduction in real exchange rate volatility. The reduction in the home bias increases the volatility of the trade balance regardless of the source of shocks. Except for the case where supply shocks lead to counter-cyclical trade balances, (i) the increase in international competition also increases the volatility of the trade balance; and (ii) the reduction in real exchange rate volatility increases the volatility of the trade balance if risk aversion is low but lowers it if risk aversion is high. The opposite applies when supply shocks lead to counter-cyclical trade balances. We calibrate the model to U.S. data and provide a quantitative assessment of the effects of increased trade in services on the trade balance.

Trade Integration and Risk Sharing

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