Analysis of panel data

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. As a consequence, richer panel data sets also have become increasingly available. This 2003 second edition is a substantial revision of the highly successful first edition of 1986. Advances in panel data research are presented in a rigorous and 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, political science, etc. Other specific revisions include the introduction of the notion of strict exogeneity with estimators presented in a generalized method of moments framework, the notion of incidental parameters, more intuitive explanations of pairwise trimming, and discussion of sample selection dynamic panel models.

Part I Introduction

1 Advantages of panel data

2 Issues involved in utilizing panel data

3 Outline of the monograph

Part II Analysis of Covariance

4 Introduction

5 Analysis of covariance

6 An example

Part III Simple Regression with Variable Intercepts

7 Introduction

8 Fixed-effects models: least-squares dummy-variable approach

9 Random-effects models: estimation of variance-components models

10 Fixed effects or random effects

11 Tests for misspecification

12 Models with specific variables and both individual- and time-specific effects

13 Heteroscedasticity

14 Models with serially correlated errors

15 Models with arbitrary error structure - Chamberlain - approach

Part IV Dynamic Models with Variable Intercepts

16 Introduction

17 The covariance estimator

18 Random-effects models

19 An example - demand for natural gas

20 Fixed effects models

21 Estimation of dynamic models with arbitrary correlations in the residuals

22 Fixed effects vector autoregressive models

Part V Simultaneous-Equations Models

23 Introduction

24 Joint generalized-least squares estimation technique

25 Estimation of structural equations

26 Triangular system

Part VI Variable-Coefficient Models

27 Introduction

28 Coefficients that vary over cross-sectional units

29 Coefficients that vary over time and cross-sectional units

30 Coefficients that evolve over time

31 Coefficients that are functions of other exogenous variables

32 A mixed fixed and random coefficients model

33 Dynamic random coefficients models

34 An example - liquidity constraints and firm investment expenditure

Part VII Discrete Data

35 Introduction

36 Some discrete-response models

37 The parametric approach to static models with heterogeneity

38 The semiparametric approach to static models

39 Dynamic models

Part VIII Truncated and Censored Data

40 Introduction

41 Nonrandomly missing data

42 Tobit models with random individual effects

43 Fixed effects estimator

44 An example: housing expenditure

45 Dynamic Tobit models

Part IX Incomplete Panel Data

46 Estimating distributed lags in short panels

47 Rotating or randomly missing data

48 Pseudo panels (or repeated cross-sectional data)

49 Pooling of a single cross-section and a single time series

Part X Miscellaneous Topics

50 Simulation methods

51 Panels with large N and T

52 Unit root tests

53 Data with multi-level structures

54 Errors of measurement

55 Modeling cross-sectional dependence

Part XI A Summary View

56 Introduction

57 Benefits and limitations of panel data

58 Efficiency of the estimates

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Table of Contents