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 article empirically examines the determinants of debt distress, defined as periods in which countries resort to any of three forms of exceptional finance: significant arrears on external debt, Paris Club rescheduling, and non-concessional International Monetary Fund lending. Probit regressions show 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. The relative importance of these factors varies with the level of development. These results are robust to a variety of alternative specifications, and the core specifications have substantial out-of-sample predictive power. The quantitative implications of these results are examined for the lending strategies of official creditors.

/pdf/when-is-external-debt-sustainable-45m9loaj8b.pdf

When is external debt sustainable

Kurtz and Schrank (hereafter KS) begin their defense by accusing us of “abandoning” our measure of Government Effectiveness in favor of other indicators such as Rule of Law. All we noted in our original response, and reiterate here, is that we find KS’s exclusive emphasis on this one particular dimension of governance to be idiosyncratic and not shared by the large economics literature on institutions and growth. We also note that we discussed all six of our governance indicators in our response simply because the critiques that KS raise of government effectiveness, notably potential respondent biases and halo effects, could equally well be applied to our other indicators. We think it important to demonstrate the scarcity of empirical support for these critiques for all six of our governance indicators.

https://www.jstor.org/stable/10.1111/j.1468-2508.2007.00552.x

Growth and Governance: A Rejoinder

Several recent papers have attempted to identify the partial effects of trade integration and institutional quality on long-run growth using the geographical determinants of trade and the historical determinants of institutions as instruments. The authors show that many of the specifications in these papers are weakly identified despite the apparently good performance of the instruments in first-stage regressions. Consequently, they argue that the cross-country variation in institutions, trade, and their geographical and historical determinants is not very informative about the partial effects of these variables on long-run growth.

/pdf/institutions-trade-and-growth-revisiting-the-evidence-1ouppamhgu.pdf

Institutions, Trade, and Growth : Revisiting the Evidence

Doing the Survey Two-Step

China, in recent years the world's largest and fastest growing economy, also has national saving rates that are among the highest in the world. This article considers a variety of statistical issues that cloud the measurement of aggregate and household saving in China. It also provides new empirical evidence on the importance of intertemporal considerations in explaining the variation in household saving across China's provinces. Given China's size and its high saving rates, it is not surprising that in recent year's considerable theoretical and empirical effort has been devoted to understanding its saving behavior. This research can be divided into two broad strands. The first emphasizes the relevance for China of traditional equilibrium theories of saving, ranging from simple Keynesian consumption/saving functions to variants of the life-cycle and permanent-income hypotheses. The second argues that equilibrium theories of saving are unlikely to be relevant in an economy in transition from plan to market. Instead, disequilibrium factors, especially shortages and rationing in goods and credit markets, explain China's saving experience. More broadly, China's saving rate remains unusually high relative to international experience, even after controlling for some of the determinants of saving.

/pdf/households-saving-in-china-gj8t5sfqe0.pdf

Households saving in China

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