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

Over the past decade the United States has experienced widening current account deficits and a steady deterioration of its net foreign asset position. During the second half of the 1990s, this deterioration was fueled by foreign investment in a booming US stock market. During the first half of the 2000s, this deterioration has been fuelled by foreign purchases of rapidly increasing US government debt. A somewhat surprising aspect of the current debate is that stock market movements and fiscal policy choices have been largely treated as unrelated events. Stock market movements are usually interpreted as reflecting exogenous changes in perceived or real productivity, while budget deficits are usually understood as a mainly political decision. We challenge this view here and develop two alternative interpretations. Both are based on the notion that a bubble (the "dot-com" bubble) has been driving the stock market, but differ in their assumptions about the interactions between this bubble and fiscal policy (the "Bush" deficits). The "benevolent" view holds that a change in investor sentiment led to the collapse of the dot-com bubble and the Bush deficits were a welfare-improving policy response to this event. The "cynical" view holds instead that the Bush deficits led to the collapse of the dot-com bubble as the new administration tried to appropriate rents from foreign investors. We discuss the implications of each of these views for the future evolution of the US economy and, in particular, its net foreign asset position.

/pdf/the-dot-com-bubble-the-bush-deficits-and-the-u-s-current-3w8wjnz1ql.pdf

The dot-com bubble, the Bush deficits, and the U.S. current account

Employment and Wages in the Public Sector: A Cross-Country Study

This paper reviews the empirical evidence on the existence of poverty traps, understood as self-reinforcing mechanisms through which poor individuals or countries remain poor. Poverty traps have captured the interest of many development policy makers, because poverty traps provide a theoretically coherent explanation for persistent poverty. They also suggest that temporary policy interventions may have long-term effects on poverty. However, a review of the reduced-form empirical evidence suggests that truly stagnant incomes of the sort predicted by standard models of poverty traps are in fact quite rare. Moreover, the empirical evidence regarding several canonical mechanisms underlying models of poverty traps is mixed.

/pdf/do-poverty-traps-exist-17on9b6g2w.pdf

Do Poverty Traps Exist

Aid works best when it is directed to countries with relatively good institutions and policies But how should good governance be measured, and how can aid allocation rules be designed in light of the strengths and weaknesses of existing measures? We address in brief a number of methodological and applied challenges, motivated by the US government's recent proposal to allocate resources from the new Millennium Challenge Account (MCA), the issues and recommendations apply more broadly Among others, we discuss the implications of margins of error in governance data, the difficulties in measuring trends, and the need to complement existing cross-country indicators with in-depth country diagnostics

Governance Indicators, Aid Allocation, and the Millennium Challenge Account

This paper summarizes the methodology of the Worldwide Governance Indicators (WGI) project, and related analytical issues. The WGI cover over 200 countries and territories, measuring six dimensions of governance starting in 1996: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, GovernmentEffectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The aggregate indicators are based on several hundred individual underlying variables, taken from a wide variety of existing data sources. The data reflect the views on governance of survey respondents and public, private, and NGO sector experts worldwide. The WGI also explicitly report margins of error accompanying each country estimate. These reflect the inherent difficulties in measuring governance using any kind of data. Even after taking these margins of error into account, the WGI permit meaningful cross-country and over-time comparisons. The aggregate indicators, together with the disaggregated underlying source data, are available at www.govindicators.org.

The worldwide governance indicators: methodology and analytical issues

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