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.

/pdf/how-to-do-xtabond2-an-introduction-to-difference-and-system-12gj9faeqd.pdf

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.

/pdf/how-to-do-xtabond2-an-introduction-to-difference-and-system-12n09sboqv.pdf

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

El mercado de trabajo espanol presenta dos caracteristicas que le confieren un caracter especial: el elevado peso del desempleo de larga duracion y la extensa utilizacion del empleo temporal. El avance del desempleo de larga duracion en Espana se produjo en el periodo 1977- 1985, durante el cual la tasa de stock se traslado rapidamente al grupo de los que llevan mas de un ano en tal situacion, con lo que la ratio de estos ultimos, que estaba en el 20 % al inicio del periodo comentado, se elevo por encima del 55 % en los ultimos anos de la decada de los ochenta. El empleo temporal, por su parte, ha experimentado una evolucion sin igual desde mediados de los anos ochenta, coincidiendo con el fomento de este tipo de contratacion a traves de la creacion de nuevas modalidades y de incentivos economicos, alcanzando la ratio de temporalidad, en los ultimos anos, niveles superiores al 30 %. En este trabajo se pretenden cubrir algunos aspectos que pueden ayudar a comprender algo mejor el funcionamiento del mercado de trabajo espanol. Por un lado, se va a centrar en estudiar los factores que afectan a las salidas del desempleo de larga duracion, ya que hay razones para pensar que este colectivo requiere un tratamiento aislado: en primer lugar, por la importancia que tiene dentro del conjunto de los desempleados; en segundo lugar, porque estudiarlo conjuntamente con los desempleados de mas corta duracion puede hacer mas dificil la identificacion de caracteristicas especificas de este grupo; y por ultimo, porque mientras que para hombres entrantes en el desempleo no parece muy relevante considerar salidas distintas a la ocupacion, para los desempleados de larga duracion se pueden plantear otras salidas, como, por ejemplo, la inactividad. Por otro lado, la dualidad existente en el mercado de trabajo espanol hace interesante el estudio de la tasa de salida hacia un empleo fijo y uno temporal por separado, a fin de caracterizar posibles diferencias: ?son significativamente distintas las tasas de salida a ambos tipos de empleo?, ?afecta el cobro de una prestacion por desempleo de la misma forma a la tasa de salida a un empleo fijo que a uno temporal?, ?y la posicion ciclica de la economia? Este trabajo se encuentra en linea con otros existentes para Espana, en particular con el de Bover, Arellano y Bentolila (1996), en el que se evaluaba el efecto del cobro de la prestacion sobre las probabilidades de salida al empleo condicionadas a la duracion del desempleo, comparado con otros factores. En dicho trabajo se contemplaban duraciones de paro hasta catorce meses y no se distinguia entre salida a empleo fijo o a empleo temporal.

Determinantes de la duración del desempleo: el paro de larga duración y la salidad a un empleo fijo

En este trabajo documentamos los cambios en la distribución de la renta de los hogares españoles en el periodo 2001-2007 utilizando datos individuales de la Encuesta Financiera de las Familias (EFF). Argumentamos que durante la VII y la VIII Legislaturas de España (2000-04 y 2004-08) se produjo un estancamiento de la renta de los hogares a pesar del crecimiento agregado de la economía. Durante este periodo también aumentó la desigualdad de la renta y disminuyó el tamaño de los hogares.

La renta de los hogares españoles en el preludio de la crisis

Participación laboral femenina en España durante los ochenta

In this Paper we propose an alternative to traditional hedonics for estimating new multiunit housing inflation, adjusting for quality changes. By relying on the within-site variation we control in a very general way for unobserved housing characteristics using site-specific effects. Precise location, transport, traffic, closeness to services, or construction quality are some of the unobserved but typically relevant housing characteristics that may bias estimated house price inflation, even when using hedonic methods. We also estimate standard hedonic equations and compare the results to those obtained with the alternative hedonic equations with site dummies. Our dataset is fairly rich in observable housing characteristics but, nevertheless, the quality-adjusted house price evolution is quite different in some cases. The data cover the construction of new housing in some of the large Spanish cities and in the smaller towns on the outskirts of the capital during part of the 1990s. (This abstract was borrowed from another version of this item.)

Olympia Bover

Papers

Determinantes de la duración del desempleo: el paro de larga duración y la salidad a un empleo fijo

La renta de los hogares españoles en el preludio de la crisis

Participación laboral femenina en España durante los ochenta

Hedonic house prices without characteristics: the case of new multiunit housing

Quality bias in the measurement of prices: empirical evidence and macroeconomic implications for Spain