The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

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Econometric Analysis of Cross Section and Panel Data

https://www.ucl.ac.uk/~uctp39a/Blundell-Bond-1998.pdf

Initial conditions and moment restrictions in dynamic panel data models

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

This working paper by CGD research fellow David Roodman provides an introduction to a particular class of econometric techniques, dynamic panel estimators. The techniques and their implementation in Stata, a statistical software package widely used in the research community, are an important input to the careful applied research CGD advocates. The techniques discussed are specifically designed to extract causal lessons from data on a large number of individuals (whether countries, firms or people) each of which is observed only a few times, such as annually over five or ten years. These techniques were developed in the 1990s by authors such as Manuel Arellano, Richard Blundell and Olympia Bover, and have been widely applied to estimate everything from the impact of foreign aid to the importance of financial sector development to the effects of AIDS deaths on households. The present paper contributes to this literature pedagogically, by providing an original synthesis and exposition of the literature on these dynamic panel estimators, and practically, by presenting the first implementation of some of these techniques in Stata. Stata is designed to encourage users to develop new commands for it, which other users can then use or even modify. In this paper Roodman introduces abar and xtabond2, which is now one of the most frequently downloaded user-written Stata commands in the world. Stata's partially open-source architecture has encouraged the growth of a vibrant world-wide community of researchers, which benefits not only from improvements made to Stata by the parent corporation, but also from the voluntary contributions of other users. Stata is arguably one of the best examples of a combination of private for-profit incentives and voluntary open-source incentives in the joint creation of a global public good.

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How to do Xtabond2: An Introduction to Difference and System GMM in Stata

The Arellano-Bond (1991) and Arellano-Bover (1995)/Blundell-Bond (1998) linear generalized method of moments (GMM) estimators are increasingly popular. Both are general estimators designed for situations with “small T, large N” panels, meaning few time periods and many individuals; with independent variables that are not strictly exogenous, meaning correlated with past and possibly current realizations of the error; with fixed effects; and with heteroskedasticity and autocorrelation within individuals. This pedagogic paper first introduces linear GMM. Then it shows how limited time span and the potential for fixed effects and endogenous regressors drive the design of the estimators of interest, offering Stata-based examples along the way. Next it shows how to apply these estimators with xtabond2. It also explains how to perform the Arellano-Bond test for autocorrelation in a panel after other Stata commands, using abar. The Center for Global Development is an independent think tank that works to reduce global poverty and inequality through rigorous research and active engagement with the policy community. Use and dissemination of this Working Paper is encouraged, however reproduced copies may not be used for commercial purposes. Further usage is permitted under the terms of the Creative Commons License. The views expressed in this paper are those of the author and should not be attributed to the directors or funders of the Center for Global Development.

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How to do xtabond2: An introduction to difference and system GMM in Stata

En este trabajo se estudia el efecto de las prestaciones por desempleo sobre el paro en Espana, destacando dos aspectos que pueden analizarse con las fuentes estadisticas disponibles. Primero revisamos y discutimos los resultados de un trabajo previo sobre la duracion del paro en que empleabamos datos de la Encuesta de Poblacion Activa durante 1987-1994, al hilo del cual discutimos varios aspectos relativos a la investigacion economica. En segundo lugar, presentamos nuevos resultados sobre el tiempo de paro con datos del Panel de Hogares de la Union Europea durante 1993-1999.

Paro y prestaciones: nuevos resultados para España

Over the last decade house prices increased remarkably in many countries. However, while in several countries there was an employment boom in the construction sector, in others the share of employment in this sector did not significantly change. In this paper we estimate a model of labor demand in the construction sector, featuring building constraints, which explains many of the international differences in the response of sectoral reallocation of employment to house prices. Countries with more building possibilities (Spain, Sweden and the US) have a high sectoral reallocation of employment, and display larger elasticities of labor demand in the construction sector with respect to house prices than countries that seem to have fewer building possibilities (Belgium, the Netherlands, and the UK). Nevertheless, our estimates imply that, for the whole economy, the elasticity of labor demand with respect to house prices is broadly similar across countries.

House prices and employment reallocation: international evidence

espanolLa Encuesta Financiera de las Familias 2014 (EFF2014) proporciona informacion detallada sobre la renta, los activos, las deudas y los gastos de los hogares espanoles referida a fi nales de 2014. Junto con las olas anteriores de 2002, 2005, 2008 y 2011, la EFF2014 permite analizar dos fases completas del ciclo economico, las cuales han tenido un fuerte impacto en la situacion fi nanciera de los hogares espanoles. Este articulo proporciona una descripcion detallada de los aspectos metodologicos mas relevantes del diseno y la implementacion de esta quinta edicion: el diseno muestral, el cuestionario, el proceso de recogida de los datos, la validacion de estos, el calculo de los pesos y la imputacion. Caracteristicas importantes tambien presentes en esta ola son el sobremuestreo de los hogares ricos y el componente panel de la muestra. EnglishThe Spanish Survey of Household Finances 2014 (EFF2014) provides detailed information on the income, assets, debt and spending of Spanish households referring to end-2014. Together with the previous waves of 2002, 2005, 2008 and 2011, the EFF2014 enables the analysis of two complete phases of the economic cycle, which have had a strong impact on the fi nancial position of Spanish households. This paper provides a detailed description of the most relevant methodological aspects in the design and implementation of this fi fth edition: the sample design, the questionnaire, the data collection process, the validation of the data, the computation of weights and the imputation procedures. Important characteristics also present in this wave are the oversampling of wealthy households and the panel component of the sample.

The spanish survey of household finances (EFF): Description and methods of the 2014 wave

This paper describes the methods of the third wave of the Spanish Survey of Household Finances (EFF2008), paying special attention to the innovations relative to the previous waves. The EFF2008 was designed to give continuity to the information on household finances collected through the EFF2002 and the EFF2005. A desirable characteristic present in all three waves is the oversampling of wealthy households. This is achieved on the basis of the wealth tax through a blind system of collaboration between the National Statistics Institute and the Tax Office which preserves stringent tax confidentiality. An additional important characteristic of the EFF is that the second and third waves have a full panel component. Further, a refreshment sample by wealth stratum has been incorporated in those two waves to preserve cross-sectional representativity and overall sample size. The EFF is the only statistical source in Spain that allows the linking of incomes, assets, debts, and consumption at the household level. The usefulness of the information contained in a survey such as the EFF has led to the decision from the European system of central banks to conduct a household wealth survey in all euro area countries following a methodology similar to the EFF. Therefore, the EFF2008 will allow harmonized comparisons with the new European wealth surveys.

The Spanish survey of household finances (eff): description and methods of the 2008 wave

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Olympia Bover

Papers

Paro y prestaciones: nuevos resultados para España

House prices and employment reallocation: international evidence

The spanish survey of household finances (EFF): Description and methods of the 2014 wave

The Spanish survey of household finances (eff): description and methods of the 2008 wave

The distribution of earnings in Spain during the 1980s: the effects of skill, unemployement, and union power