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

What are the effects of increased trade in goods and services on the trade balance? We study the effects of reducing transport costs in a Ricardian model with complete asset markets and find that this increases the volatility of the trade balance. This result applies regardless of whether supply or demand shocks are the main source of economic fluctuations. Both type of shocks generate fluctuations in the trade balance that are in part moderated by stabilizing movements in the terms of trade. Trade integration dampens these terms of trade movements and, for a given distribution of shocks, amplifies fluctuations in the trade balance. To overturn this result, one must assume that either trade integration is sufficiently biased towards goods with strong comparative advantage and/or risk aversion is sufficiently extreme. We calibrate the model to U.S. data and find that, for reasonable parameter values, increased trade in services could double the volatility of the trade balance.

/pdf/trade-integration-and-risk-sharing-49pqhpemq6.pdf

Trade Integration and Risk Sharing

Multilateral development banks are sometimes thought to engage in "defensive lending" -- the practice of extending new loans purely in order to ensure that existing loans are repaid. We empirically examine this hypothesis using data on lending by and repayments to the International Development Association (IDA), which is the largest multilateral provider of concessional development loans to low-income countries. We argue that key institutional features of IDA both (i) potentially create incentives for defensive lending and also (ii) enable particularly sharp tests of the defensive lending hypothesis. We find that there is a surprisingly robust partial correlation between disbursements on new IDA loans and repayments on existing loans. However, a closer look at the evidence suggests that defensive lending is unlikely to be a major explanation for this relationship.

http://www.rei.unipg.it/rei/article/download/73/83

Does IDA Engage in Defensive Lending

Aid donors and recipients have long been concerned that aid inflows may lead to an appreciation of the real exchange rate and an associated loss of competitiveness. This paper provides new evidence of the dynamic effects of aid on the real exchange rate, using an identification strategy that exploits the long delays between the approval of aid projects and the subsequent disbursements on them. These disbursement delays enable the isolation of a source of variation in aid inflows that is uncorrelated with contemporaneous macroeconomic shocks that may drive both aid and the real exchange rate. Using this predetermined component of aid as an instrument, there is little evidence that aid inflows lead to significant real exchange rate appreciations.

/pdf/aid-disbursement-delays-and-the-real-exchange-rate-3nkzbu2cm1.pdf

Aid, Disbursement Delays, and the Real Exchange Rate

In many empirical applications involving combined time-series and cross-sectional data, the residuals from different cross-sectional units are likely to be correlated with one another This is the case in applications in macroeconomics and international economics where the cross-sectional units may be countries, states, or regions observed over time Spatial correlations among such cross-sections may arise for a number of reasons, ranging from observed common shocks such as terms of trade oil shocks, to unobserved contagion or neighborhood effects which propagate across countries in complex ways The authors observe that presence of such spatial correlations in residuals complicates standard inference procedures that combine time-series and cross-sectional data since these techniques typically require the assumption that the cross-sectional units are independent When this assumption is violated, estimates of standard errors are inconsistent, and hence are not useful for inference And standard correction for spatial correlations will be valid only if spatial correlations are of particular restrictive forms The authors propose a correlation for spatial correlations that does not require strong assumptions concerning their form and how show it is superior to a number of commonly used alternatives

Spatial correlations in panel data

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 weaklyidentified 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.

Institutions, trade, and growth: revisiting the evidence

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