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.

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Econometric Analysis of Panel Data

Mitigation from a cross-sectoral perspective

Estimating long-run relationships from dynamic heterogeneous panels☆

This chapter discusses leading problems linked to energy that the world is now confronting and to propose some ideas concerning possible solutions. Oil deserves special attention among all energy sources. Since the beginning of 1981, it has merely been continuing and enhancing the downward movement in consumption and prices caused by excessive rises, especially for light crudes such as those from Africa, and the slowing down of worldwide economic growth. Densely-populated oil-producing countries need to produce to live, to pay for their food and their equipment. If the economic growth of the industrialized countries were to be 4%, even if investment in the rational use of energy were pushed to the limit and the development of nonpetroleum energy sources were also pursued actively, it would be extremely difficult to prevent a sharp rise in prices. It is evident that it is absolutely necessary to pursue actively the development of coal, natural gas, and nuclear power if a physical shortage of energy is not to block economic growth.

World energy outlook

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On Approximating the Statistical Properties of Elasticities

Results of earlier transcendental logarithmic (translog) production function are challenged on the basis of the limited time-series used. The same methodology is applied to a pool of international manufacturing data to test whether long-run price elasticities can be generated with intercountry samples. Both the time-series and international cross-section methods agree that there is some elasticity of substitution between energy demand and non-energy inputs and that energy forecasting should not be based on the assumption that substitutions between energy and nonenergy inputs are trivial. The international method finds, however, that capital and energy are substitutes rather than complements in the long-run and that there is a difference between short-run and long-run effects of substitution. 29 references. (DCK)

An Intercountry Translog Model of Energy Substitution Responses

In a 1997 paper, Borenstein, Cameron, and Gilbert (BCG) claim that gasoline prices rise quickly following an increase in the price of crude oil, but fall slowly following a decrease. This note estimates an error-correction model with daily spot gasoline and crude-oil price data over the period 1985–1998 and finds no evidence of asymmetry in wholesale gasoline prices. The sources of the difference in results are twofold. First, we use the standard Engle-Granger two-step estimation procedure, whereas BCG used a nonstandard estimation methodology. Second, even using BCG's nonstandard specification, the use of daily rather than weekly data yields little evidence of price asymmetry.

New evidence on asymmetric gasoline price responses

Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline

This paper outlines a procedure for estimating a general index of technical change within the context of a quite general production technology. Specifically, when panel data are available for firms in an industry, time-specific dummies can be combined in a nonlinear estimation procedure to yield a general index of technical change that may be both nonneutral and scale augmenting. This approach offers numerous advantages over the traditional time trend representation of technical change. For example, the general index can serve as the basis for analysis of the determinants of technical change. Results for a sample of 30 electric utilities over the period 1951-78 show that the productivity decline of the 1970s can be attributed primarily to sulphur oxide restrictions and secularly declining capacity utilization due to rapidly increasing peak-load demands.

A General Index of Technical Change

This paper reexamines the benefits of pooling and, in addition, contrasts the performance of newly proposed heterogeneous estimators. The analysis utilizes a panel data set from 46 American states over the period 1963 to 1992 and a dynamic demand specification for cigarettes. Also, the forecast performance of the various estimators is compared.

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James M. Griffin

Papers

An Intercountry Translog Model of Energy Substitution Responses

New evidence on asymmetric gasoline price responses

Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline

A General Index of Technical Change

To pool or not to pool: homogeneous versus heterogeneous estimators applied to cigarette demand