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 Egyptian pound depreciated sharply between 2000 and 2005, declining by 26 percent in nominal trade-weighted terms The author investigates the effect of the large depreciation on household welfare operating through exchange rate-induced changes in consumer prices He estimates exchange rate pass-through regressions using disaggregated monthly consumer price indices to isolate the impact of the exchange rate changes on consumer prices Then he uses household-level data from the 2000 and 2005 Egyptian household surveys to quantify the welfare effects of these consumer price changes at the household level The average welfare loss due to exchange rate-induced price increases was equivalent to 74 percent of initial expenditure Stronger estimated exchange rate pass-through for food items imply that this effect disproportionately affected poorer households

/pdf/the-welfare-effects-of-a-large-depreciation-the-case-of-59ws6atr1m.pdf

The welfare effects of a large depreciation : the case of Egypt, 2000-05

This paper uses a novel loan-level dataset covering lending by official creditors to developing country governments to construct an instrument for public spending that can be used to estimate government spending multipliers. Loans from official creditors (primarily multilateral development banks and bilateral aid agencies) are a major source of financing for government spending in developing countries. These loans typically finance public spending projects that take several years to implement, with multiple disbursements linked to the stages of project implementation. The long disbursement periods for these loans imply that the bulk of government spending financed by official creditors in a given year reflects loan approval decisions made in many previous years, before current-year macroeconomic shocks are known. Loan-level commitment and disbursement transactions from the World Bank's Debtor Reporting System database are used to isolate a predetermined component of government spending associated with past loan approvals. This can be used as an instrument to estimate spending multipliers for a large sample of 102 developing countries. The one-year government spending multiplier is reasonably-precisely estimated to be around 0.4, and there is some suggestive evidence that multipliers are larger in recessions, in countries less exposed to international trade, and in countries with flexible exchange rate regimes.

Government Spending Multipliers in Developing Countries: Evidence from Lending by Official Creditors

It is well known that business cycles in OECD countries exhibit a remarkable degree of synchronization. Much less known is that the peak of the OECD cycle is associated with high prices of labour-intensive products and low prices of capital-intensive ones. We document this cyclical behavior of product prices and argue that it offers an important clue as to why business cycles are so synchronized. Positive shocks in one or more countries raise the prices of labour-intensive products and, as a result, the demand for labour throughout the industrialized world. This generates increases in wages, employment and output in all industrial countries. Through this channel, shocks are positively transmitted across countries, creating a force towards the synchronization of business cycles.

/pdf/product-prices-and-the-oecd-cycle-4bgdr6m6up.pdf

Product Prices and the OECD Cycle

The authors examine the empirical evidence in support of the poverty trap view of underdevelopment. They calibrate simple aggregate growth models in which poverty traps can arise due to either low saving or low technology at low levels of development. They then use these models to assess the empirical relevance of poverty traps and their consequences for policy. The authors find little evidence of the existence of poverty traps based on these two broad mechanisms. When put to the task of explaining the persistence of low income in African countries, the models require either unreasonable values for key parameters, or else generate counterfactual predictions regarding the relations between key variables. These results call into question the view that a large scaling-up of aid to the poorest countries is a necessary condition for sharp and sustained increases in growth.

Poverty traps, aid, and growth

The identification of structural parameters in the linear instrumental variables (IV) model is typically achieved by imposing the prior identifying assumption that the error term in the structural equation of interest is orthogonal to the instruments. Since this exclusion restriction is fundamentally untestable, there often are legitimate doubts about the extent to which the exclusion restriction holds. In this paper I illustrate the effects of such prior uncertainty about the validity of the exclusion restriction on inferences based on linear IV models. In particular, I provide a mapping from prior uncertainty about the exclusion restriction into increased uncertainty about parameters of interest. Moderate prior uncertainty about exclusion restrictions can lead to a substantial loss of precision in estimates of structural parameters. This loss of precision is relatively more important in situations where IV estimates appear to be more precise, for example in larger samples or with stronger instruments. I illustrate these points using several prominent recent empirical papers that use linear IV models. An accompanying electronic table allows users to readily explore the robustness of inferences to uncertainty about the exclusion restriction in their particular applications.

Instrumental Variables Regressions with Uncertain Exclusion Restrictions

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