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

The World Development Report (WDR) 2002 is about building market institutions that promote growth and reduce poverty, addressing how institutions support markets, what makes institutions work, and how to build them. This theme is a natural a natural continuation of the WDR 2000/01, which discussed the central role of markets in the lives of poor people, leaving important issues for the WDR 2003, which will focus on the development of human, natural, and environmental capital. This report emphasizes on building institutions to support the development of markets, and its main messages address supplying effective institutions, and creating the demand for them, through: a design that complements institutions, human capabilities, and available technologies; innovations that identify institutional successes, and weaknesses, by experimenting with, and recognizing local conditions, and differences, ranging from social norms, to geography; a connection of communities of market players, by opening information flows, and trade; and, the promotion of competition among jurisdictions, firms, and individuals. The report is an introspective analysis about how do institutions support markets, growth, and poverty reduction, defining institutional work as a channel of information about market conditions, goods, and participants; as a mean to enforce property rights, and contracts; and, as a tool to increase competition in markets. The report offers lessons derived from experience in institutional evolution, and, although it does not address all possible institutional problems, it does focus on sets of institutions from many fields, to show that the framework, and messages can be applied regardless of the sector.

Rapport sur le developpement dans le monde 2002 : des institutions pour les marches

This paper proposes a novel method of isolating fluctuations in public spending that are likely to be uncorrelated with contemporaneous macroeconomic shocks and can be used to estimate government spending multipliers. The approach relies on two features unique to many low-income countries: (1) borrowing from the World Bank finances a substantial fraction of public spending, and (2) actual spending on World Bank-financed projects is typically spread out over several years following the original approval of the project. These two features imply that fluctuations in spending on World Bank projects in a given year are in large part determined by fluctuations in project approval decisions made in previous years, and so are unlikely to be correlated with shocks to output in the current year. World Bank project-level disbursement data are used to isolate the component of public spending associated with project approvals from previous years, which in turn can be used to estimate government spending multipliers, in a sample of 29 aid-dependent low-income countries. The estimated multipliers are small, reasonably precisely estimated, and rarely significantly different from zero.

How large is the government spending multiplier ? evidence from World Bank lending

Faced with income fluctuations, countries smooth their consumption by raising savings when income is high, and vice versa. How much of these savings do countries invest at home and abroad? In other words, what are the effects of fluctuations in savings on domestic investment and the current account? In the long run, we find that countries invest the marginal unit of savings in domestic and foreign assets in the same proportions as in their initial portfolio, so that the latter is remarkably stable. In the short run, we find that countries invest the marginal unit of savings mostly in foreign assets, and only gradually do they rebalance their portfolio back to its original composition. This means that countries not only try to smooth consumption, but also domestic investment. To achieve this, they use foreign assets as a buffer stock.

Current Accounts in the Long and Short Run

Between 1989 and 2003, low-income countries received $100 billion in debt relief. The stated objectives for much of this debt relief have been to reduce debt overhang and to free up recipient government resources for development spending that would otherwise have been used for debt service. In this paper we empirically assess the extent to which debt relief has been successful in meeting these objectives, using a newly-constructed database measuring the present value of debt relief for 62 low-income countries. We find little evidence that debt relief has affected the level and composition of public spending in recipient countries. We also do not find evidence that debt relief has raised growth, investment rates or the quality of policies and institutions among recipient countries. Although we cannot rule out the possibility that our failure to find evidence of positive impacts of debt relief is due to a variety of data and statistical problems, the evidence reported here does suggest that some skepticism is in order regarding the likely benefits of further large-scale debt relief.

What Has 100 Billion Dollars Worth of Debt Relief Done for Low- Income Countries?

The World Bank Economic Review 14 (3), Sept 2000

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