Econometric Analysis of Panel Data (Springer Texts in Business and Economics)

Preface
	References
Contents
1 Introduction
	1.1 Panel Data: Some Examples
		1.1.1 Examples of Micro-panels
		1.1.2 Examples of Macro-panels
		1.1.3 Some Basic References
	1.2 Why Should We Use Panel Data? Their Benefits and Limitations
	1.3 Note
	References
2 The One-Way Error Component Regression Model
	2.1 Introduction
	2.2 The One-Way Fixed Effects Model
	2.3 The One-Way Random Effects Model
	2.4 Maximum Likelihood Estimation
	2.5 Prediction
	2.6 Examples
		2.6.1 Example 1: Investment Equation
		2.6.2 Example 2: Gasoline Demand Equation
		2.6.3 Example 3: Public Capital Productivity
	2.7 Selected Applications
	2.8 Computational Note
	2.9 Notes
	2.10 Problems
3 The Two-Way Error Component Regression Model
	3.1 Introduction
	3.2 The Two-Way Fixed Effects Model
		3.2.1 Testing for Fixed Effects
	3.3 The Two-Way Random Effects Model
		3.3.1 Monte Carlo Results
	3.4 Maximum Likelihood Estimation
	3.5 Prediction
	3.6 Examples
		3.6.1 Example 1: Investment Equation
		3.6.2 Example 2: Gasoline Demand Equation
		3.6.3 Example 3: Public Capital Productivity
	3.7 Computational Note
	3.8 Notes
	3.9 Problems
4 Test of Hypotheses with Panel Data
	4.1 Tests for Poolability
		4.1.1 Test for Poolability Under uthicksimN(0,σ 2INT)
		4.1.2 Test for Poolability Under the General Assumption uthicksimN(0,Ω)
		4.1.3 Examples
	4.2 Tests for Individual and Time Effects
		4.2.1 The Breusch–Pagan Test
		4.2.2 Honda, King and Wu, and the Standardized Lagrange Multiplier Tests
		4.2.3 Gourieroux, Holly and Monfort Test
		4.2.4 Conditional LM Tests
		4.2.5 ANOVA F and the Likelihood Ratio Tests
		4.2.6 Monte Carlo Results
		4.2.7 An Illustrative Example
	4.3 Hausman's Specification Test
		4.3.1 Example 1: Investment Equation
		4.3.2 Example 2: Gasoline Demand Equation
		4.3.3  Example 3: Canadian Manufacturing Industries
		4.3.4 Example 4: Sawmills in Washington State
		4.3.5 Example 5: Mariage Premium
		4.3.6 Example 6: Currency Union
		4.3.7 Hausman's Test for the Two-Way Model
	4.4 Further Reading
	4.5 Notes
	4.6 Problems
5 Heteroskedasticity and Serial Correlation in the Error Component Model
	5.1 Heteroskedasticity
		5.1.1 Testing for Homoskedasticity in an Error Component Model
	5.2 Serial Correlation
		5.2.1 The AR(1) Process
		5.2.2 The AR(2) Process
		5.2.3 The AR(4) Process for Quarterly Data
		5.2.4 The MA(1) Process
		5.2.5 Unequally Spaced Panels with AR(1) Disturbances
		5.2.6 Prediction
		5.2.7 Testing for Serial Correlation and Individual Effects
	5.3 Time-Wise Autocorrelated and Cross-Sectionally Heteroskedastic Panel Regression
	5.4 Further Reading
	5.5 Notes
	5.6 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
	6.4 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 Further Reading
	7.7 Notes
	7.8 Problems
8 Dynamic Panel Data Models
	8.1 Introduction
	8.2 The Arellano and Bond Estimator
		8.2.1 Testing for Over-Identification Restrictions and Serial Correlation in Dynamic Panel Models
		8.2.2 Downward Bias of the Estimated Asymptotic Standard Errors
		8.2.3 Too Many Moment Conditions and the Bias Efficiency Trade-Off
	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 Limited Information Maximum Likelihood
	8.8 Empirical Examples
		8.8.1 Example 1: Dynamic Demand for Cigarettes
		8.8.2 Example 2: Democracy and Education
	8.9 Selected Applications
	8.10 Further Reading
	8.11 Notes
	8.12 Problems
9 Unbalanced Panel Data Models
	9.1 Introduction
	9.2 The Unbalanced One-Way Error Component Model
		9.2.1 ANOVA Methods
	9.3 Maximum Likelihood Estimators
		9.3.1 Minimum Norm and Minimum Variance Quadratic Unbiased Estimators (MINQUE and MIVQUE)
		9.3.2 Monte Carlo Results
	9.4 Empirical Example: Hedonic Housing
	9.5 The Unbalanced Two-Way Error Component Model
		9.5.1 The Fixed Effects Model
		9.5.2 The Random Effects Model
	9.6 Testing for Individual and Time Effects Using Unbalanced Panel Data
	9.7 The Unbalanced Nested Error Component Model
		9.7.1 Empirical Example: Nested States Public Capital Productivity
	9.8 Notes
	9.9 Problems
10 Special Topics
	10.1 Measurement Error and Panel Data
	10.2 Rotating Panels
	10.3 Pseudo-Panels
	10.4 Short-Run Versus Long-Run Estimates in Pooled Models
	10.5 Heterogeneous Panels
	10.6 Count Panel Data
	10.7 Notes
	10.8 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
	11.9 Notes
	11.10 Problems
12 Nonstationary Panels
	12.1 Introduction
	12.2 Panel Unit Roots Tests Assuming Cross-Sectional Independence
		12.2.1 Levin, Lin and Chu Test
		12.2.2 Im, Pesaran and Shin Test
		12.2.3 Breitung's Test
		12.2.4 Combining p-Value Tests
		12.2.5 Residual-Based LM Test
	12.3 Panel Unit Roots Tests Allowing for Cross-Sectional Dependence
	12.4 Spurious Regression in Panel Data
	12.5 Panel Cointegration Tests
		12.5.1 Residual-Based DF and ADF Tests (Kao Tests)
		12.5.2 Residual-Based LM Test
		12.5.3 Pedroni Tests
		12.5.4 Likelihood-Based Cointegration Test
		12.5.5 Finite Sample Properties
	12.6 Estimation and Inference in Panel Cointegration Models
	12.7 Empirical Examples
		12.7.1 Example 1: Purchasing Power Parity
		12.7.2 Example 2: International R&D Spillover
		12.7.3 Example 3: OECD Health Care Expenditures
	12.8 Further Reading
	12.9 Notes
	12.10 Problems
13 Spatial Panel Data Models
	13.1 Introduction
	13.2 Spatial Error Component Regression Model
	13.3 Spatial Lag Panel Data Regression Model
	13.4 Forecasts Using Panel Data with Spatial Error Correlation
	13.5 Panel Unit Root Tests and Spatial Dependence
	13.6 Panel Data Tests for Cross-Sectional Dependence
	13.7 Computational Note
	13.8 Problems