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

Migraciones regionales en España

We review the different migration episodes in Spain since the beginning of the 20 th century, with special emphasis on the pieces of empirical work that have tried to identify the factors behind them. Migrations abroad for the 1900-1950 and 1950-1973 periods are considered. Foreign immigration is also discussed. Special attention is devoted to internal migration, both to inter-regional and intra-regional flows. Since the early 1980’s net inter-regional flows have fallen and, following the expansion of the welfare state, the traditionally poor and high unemployment regions have become net immigration regions, while the reverse has happened with the better off ones. Intra-regional migration, which has not received much attention so far, is also analysed to understand its spectacular increase in all regions since 1982. This is associated to the increased employment opportunities in the services sector that has prompted moves (mainly within regions), mostly of skilled workers, towards larger towns where the new jobs are.

Migrations in Spain: historical background and current trends

The Survey of Financial Competences (ECF) is a joint initiative of the Banco de Espana and the Comision Nacional del Mercado de Valores (CNMV) aimed at measuring the financial competences of the adult population in Spain. Between 2016 and 2017, information has been collected for a large sample provided by the National Statistical Institute of randomly selected individuals, and representative of the whole Spanish population between 18 and 79 years of age and of each of its regions. This paper provides a detailed description of the most relevant methodological aspects in the design and implementation of the survey: the sample design, the questionnaire, the data collection process, the validation of the data, the computation of weights, and the imputation procedure.

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The Survey of Financial Competences (ECF): Description and Methods of the 2016 Wave

The EFF collects detailed information on household assets, debts, income, consumption, and demographic variables. One important characteristic of this survey is that it oversamples high-wealth households. Another important characteristic of the EFF is that the second wave has a full panel component that provides statistical information on transitions among states and individual changes in the variables. The purpose of this work is to use the panel data of the EFF2002-2005 to describe the changes in income, asset holdings, and wealth of Spanish households between the end of 2002 and 2005.

Olympia Bover

Papers

Migraciones regionales en España

Migrations in Spain: historical background and current trends

The Survey of Financial Competences (ECF): Description and Methods of the 2016 Wave

The dynamics of household income and wealth: results from the panel of the Spanish Survey of Household Finances (EFF) 2002-2005

Lessons from the Spanish survey of household finances