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

Fiscal Policy as a Tool for Stabilization in Developing Countries

This paper develops a structural approach for modeling how respondents answer survey questions and uses it to estimate the proportion of respondents who are reticent in answering corruption questions, as well as the extent to which reticent behavior biases down conventional estimates of corruption The context is a common two-step survey question, first inquiring whether a government official visited a business, and then asking about bribery if a visit was acknowledged Reticence is a concern for both steps, since denying a visit sidesteps the bribe question This paper considers two alternative models of how reticence affects responses to two-step questions, with differing assumptions on how reticence affects the first question about visits Maximum-likelihood estimates are obtained for seven countries using data on interactions with tax officials Different models work best in different countries, but cross-country comparisons are still valid because both models use the same structural parameters On average, 40 percent of corruption questions are answered reticently, with much variation across countries A statistic reflecting how much standard measures underestimate the proportion of all respondents who had a bribe interaction is developed The downward bias in standard measures is highly statistically significant in all countries, varying from 12 percent in Nigeria to 90 percent in Turkey The source of bias varies widely across countries, between denying a visit and denying a bribe after admitting a visit

Doing the survey two-step : the effects of reticence on estimates of corruption in two-stage survey questions

In regard to Indonesia's economic and social situation, the last six months have shown a pick-up in the momentum of growth. This paper includes the following headings: summary introduction; growth, investment, and employment; financial markets; external sector and external debt; inflation and monetary policy; labor markets and wages; fiscal policy and government debt; financial sector development; poverty and the Millennium Development Goals (MDGs); policy priorities and developments; and the outlook and risks.

Indonesia : economic and social update (November 2007)

The authors use preliminary results from an ongoing effort to construct estimates of debt relief to study its allocation across a sample of 62 low-income countries. They find some evidence that debt relief, particularly from multilateral creditors, has been allocated to countries with better policies in recent years. Somewhat surprisingly, conditional on per capita incomes and policy, more indebted countries are not much more likely to receive debt relief. But countries that have large debts especially to multilateral creditors are more likely to receive debt relief. The authors do not find much evidence that debt relief responds to shocks to GDP growth. Finally, most of the persistence in debt relief is driven by slowly changing country characteristics, indicating that it may be difficult for countries to exit from cycles of repeated debt relief.

Who Gets Debt Relief

China's economic success over the past three decades can be decomposed into three broad contributions to growth; accumulation of labor and capital, growth induced by structural transformation (i.e. the reallocation of labor and capital across sectors and ownership units), and growth in total factor productivity. Understanding the evolution of these three growth determinants is important for understanding Chinas future growth potential. For example, in the neoclassical growth model, rapid growth through factor accumulation eventually slows with the onset of diminishing returns. And growth achieved through the reallocation of factors of production from less efficient to more efficient uses will also eventually peter out as marginal products of factors are equated across units. In this paper we perform a growth accounting exercise for China which allows us to separate these three broad contributions to growth. The main novelty of our exercise lies in our efforts to understand the role of reallocation of both capital and labor across major sectors (agriculture, industry, and services), and across ownership forms (state, collective, and other).

Growth in China 1978-2008 : Factor Accumulation, Factor Reallocation, and Improvements in Productivity

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