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

http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/12/15/000016406_20051215161243/Rendered/PDF/wps3801.pdf

Neither a borrower nor a lender : does China's zero net foreign asset position make economic sense?

Kraay and Nehru empirically examine the determinants of "debt distress," which they define as periods in which countries resort to exceptional finance in any of three forms: (1) significant arrears on external debt, (2) Paris Club rescheduling, and (3) nonconcessional International Monetary Fund lending. Using probit regressions, the authors find that three factors explain a substantial fraction of the cross-country and time-series variation in the incidence of debt distress: the debt burden, the quality of policies and institutions, and shocks. They show that these results are robust to a variety of alternative specifications, and that their core specifications have substantial out-of-sample predictive power. The authors also explore the quantitative implications of these results for the lending strategies of official creditors. This paper - a joint product of Investment Climate, Development Research Group, and Heavily Indebted Poor Countries (Africa and Latin America), Poverty Reduction and Economic Management Network - is part of a larger effort in the Bank to study debt sustainability issues.

When is External Debt Sustainable

The Worldwide Governance Indicators, reporting estimates of six dimensions of governance for over 200 countries between 1996 and 2005, have become widely used among policymakers and academics. They have also attracted some explicit written criticisms. In this short paper the authors synthesize 11 critiques offered by four recent papers. They then refute them as either conceptually incorrect or empirically unsubstantiated.

The worldwide governance indicators project: answering the critics

In recent years the growing interest of academics and policymakers in governance has been reflected in the proliferation of cross-country indices measuring various aspects of governance. The authors explain how a simple variant of an unobserved components model can be used to combine the information from these different sources into aggregate governance indicators. The main advantage of this method us that it allows quantification of the precision of both individual sources of governance data and country-specific aggregate governance indicators. The authors illustrate the methodology by constructing aggregate indicators of bureaucratic quality, rule of law, and graft for a sample of 160 countries. Although these aggregate governance indicators are more informative about the level of governance than any single indicator, the standard errors associated with estimates of governance are still large relative to the units in which governance is measured. In light of these margins of error, it is misleading to offer very precise rankings of countries according to their level of governance: small differences in country rankings are unlikely to be statistically - let alone practically - significant. Nevertheless, these aggregate governance indicators are useful because they allow countries to be sorted into broad groupings according to levels of governance, and they can be used to study the causes and consequences of governance in a much larger sample of countries than previously used (see for example the companion paper by the authors,"Governance matters", Policy Research Working Paper no. 2196).

Aggregating governance indicators

The authors present estimates of six dimensions of governance covering 199 countries and territories for four time periods: 1996, 1998, 2000, and 2002. These indicators are based on several hundred individual variables measuring perceptions of governance, drawn from 25 separate data sources constructed by 18 different organizations. The authors assign these individual measures of governance to categories capturing key dimensions of governance and use an unobserved components model to construct six aggregate governance indicators in each of the four periods. They present the point estimates of the dimensions of governance as well as the margins of errors for each country for the four periods. The governance indicators reported here are an update and expansion of previous research work on indicators initiated in 1998 (Kaufmann, Kraay, and Zoido-Lobat 1999a,b and 2002). The authors also address various methodological issues, including the interpretation and use of the data given the estimated margins of errors.

Government matters III: governance indicators for 1996-2002

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