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Analysis of Panel Data

Опубликовано на портале: 23-07-2004
Cambridge: Cambridge University Press, 2002
Тематический раздел:
Panel data models have become increasingly popular among applied researchers due to their heightened capacity for capturing the complexity of human behavior, as compared to cross-sectional or time series data models. This second edition represents a substantial revision of the highly successful first edition (1986). Recent advances in panel data research are presented in an accessible manner and are carefully integrated with the older material. The thorough discussion of theory and the judicious use of empirical examples make this book useful to graduate students and advanced researchers in economics, business, sociology and political science.

"Cheng Hsiao has made many significant and important contributions to panel data econometrics, both methodological and applied, beginning with his 1972 dissertation, in numerous articles, and in his masterful and magisterial 1986 monograph, long a standard reference and popular graduate text. (cont.)

Not only has Hsiao significantly revised the material covered in his original monograph, he has added major new chapters on nonlinear panel models of discrete choice and sampl selection, and included new material on the Bayesian treatment of models with fixed and random coefficients, pseudopanels, simulation methods of estimation, and more extensive treatment of dynamic models. Throughout, Hsiao provides applied examples, which greatly enhance the reader's understanding and intuition. The clarity of his exposition and organization is exemplary. All of us who work in the field of panel data econometrics have been, and will now more than ever continue to be in Hsiao's debt." Marc Nerlove, University of Maryland

"The literature on panel data modeling has seen unprecendented growth over the past decade and Cheng Hsiao, himself one of the leading contributors to this literature, is to be congratulated for providing us with a comprehensive and timely update of his classic text. This version not only presents a substantial revision of the 1986 edition, but also offers major additions covering non-linear panel data models dealing with useful overviews of unit root and cointegration in dynamic heterogeneous panels. It should prove invaluable to students and teachers of advanced undergraduate and graduate economic courses." Hashem Perasan, Trinity College, Cambridge

"The first edition of Analysis of Panel Data by Cheng Hsiao has been necessary reading and a landmark for 15 years. The revised and much expanded second edition splendidly integrates the important new developments in the field. One can be sure it will stay a landmark for 15 years to come." Jacques Mairesse, ENSAE, France

"Cheng Hsiao has done a great service to the profession by expanding his highly successful first edition to include the important results that have been obtained by him and other researchers since the publication of the first edition. Escpecially noteworthy in this edition is the application of panel data analysis to qualitative response and sample selection models. Cheng has admirably succeeded in presenting the mathematical results both rigorously and lucidly. Many theoretical results are illustrated by interesting empirical examples. This edition should prove to be an extremely useful reference for the experts in the field as well as graduate students." Takeshi Amemiya, Stanford University

1. Introduction:

    1.1 Advantages of panel data
    1.2 Issues involved in utilizing panel data
    1.3 Outline of the monograph

2. Analysis of covariance:

    2.1 Introduction
    2.2 Analysis of covariance
    2.3 An example

3. Simple regression with variable intercepts:

    3.1 Introduction
    3.2 Fixed-effects models: least-squares dummy-variable approach
    3.3 Random-effects models: estimation of variance-components models
    3.4 Fixed effects or random effects
    3.5 Tests for misspecification
    3.6 Models with specific variables and both individual- and time-specific effects
    3.7 Heteroscedasticity
    3.8 Models with serially correlated errors
    3.9 Models with arbitrary error structure Chamberlain -approach

4. Dynamic models with variable intercepts:

    4.1 Introduction
    4.2 The covariance estimator
    4.3 Random-effects models
    4.4 An example - demand for natural gas
    4.5 Fixed effects models
    4.6 Estimation of dynamic models with arbitrary correlations in the residuals
    4.7 Fixed effects vector autoregressive models

5. Simultaneous-equations models:

    5.1 Introduction
    5.2 Joint generalized-least squares estimation technique
    5.3 Estimation of structural equations
    5.4 Triangular system

6. Variable-coefficient models:

    6.1 Introduction
    6.2 Coefficients that vary over cross-sectional units
    6.3 Coefficients that vary over time and cross-sectional units
    6.4 Coefficients that evolve over time
    6.5 Coefficients that are functions of other exogenous variables
    6.6 A mixed fixed and random coefficients model
    6.7 Dynamic random coefficients models
    6.8 An example - liquidity constraints and firm investment expenditure

7. Discrete data:

    7.1 Introduction
    7.2 Some discrete-response models
    7.3 The parametric approach to static models with heterogeneity
    7.4 The semiparametric approach to static models
    7.5 Dynamic models

8. Truncated and censored data:

    8.1 Introduction
    8.2 Nonrandomly missing data
    8.3 Tobit models with random individual effects
    8.4 Fixed effects estimator
    8.5 An example: housing expenditure
    8.6 Dynamic Tobit models

9. Incomplete panel data:

    9.1 Estimating distributed lags in short panels
    9.2 Rotating or randomly missing data
    9.3 Pseudo panels (or repeated cross-sectional data)
    9.4 Pooling of a single cross-section and a single time series

10. Miscellaneous topics:

    10.1 Simulation methods
    10.2 Panels with large N and T
    10.3 Unit root tests
    10.4 Data with multi-level structures
    10.5 Errors of measurement
    10.6 Modeling cross-sectional dependence

11. A summary view:

    11.1 Introduction
    11.2 Benefits and limitations of panel data
    11.3 Efficiency of the estimates.
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