دانلود کتاب Applied Econometrics: A Practical Guide (Routledge Advanced Texts in Economics and Finance)

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Cover
Half Title
Series
Title
Copyright
Contents
List of figures
List of tables
Preface
Acknowledgments
1 Review of estimation and hypothesis tests
1.1 The problem
1.2 Population and sample
1.3 Hypotheses
1.4 Test statistic and its sampling distribution
1.5 Type I and Type II errors
1.6 Significance level
1.7 p-value
1.8 Powerful tests
1.9 Properties of estimators
1.10 Summary
Review questions
2 Simple linear regression models
2.1 Introduction
2.1.1 A hypothetical example
2.1.2 Population regression line
2.1.3 Stochastic specification for individuals
2.2 Ordinary least squares estimation
2.3 Coefficient of determination (R2)
2.3.1 Definition and interpretation of R2
2.3.2 Application of R2: Morck, Yeung and Yu (2000)
2.3.3 Application of R2: Dechow (1994)
2.4 Hypothesis test
2.4.1 Testing H0 : β1 = 0 vs. H1 : β1 ≠ 0
2.4.2 Testing H0 : β1 = c vs. H1 : β1 ≠ c (c is a constant)
2.5 The model
2.5.1 Key assumptions
2.5.2 Gauss-Markov Theorem
2.5.3 Consistency of the OLS estimators
2.5.4 Remarks on model specification
2.6 Functional forms
2.6.1 Log-log linear models
2.6.2 Log-linear models
2.7 Effects of changing measurement units and levels
2.7.1 Changes of measurement units
2.7.2 Changes in the levels
2.8 Summary
Review questions
References
Appendix 2 How to use EViews, SAS and R
3 Multiple linear regression models
3.1 The basic model
3.2 Ordinary least squares estimation
3.2.1 Obtaining the OLS estimates
3.2.2 Interpretation of regression coefficients
3.3 Estimation bias due to correlated-omitted variables
3.4 R2 and the adjusted R2
3.4.1 Definition and interpretation of R2
3.4.2 Adjusted R2
3.5 Hypothesis test
3.6 Model selection
3.6.1 General-to-simple approach
3.6.2 A comment on hypothesis testing
3.6.3 Guidelines for model selection
3.7 Applications
3.7.1 Mitton (2002)
3.7.2 McAlister, Srinivasan and Kim (2007)
3.7.3 Collins, Pincus and Xie (1999)
3.7.4 Angrist and Pixchke (2009, pp. 64–68)
3.8 Summary
Review questions
References
Appendix 3A Hypothesis test using EViews and SAS
Appendix 3B Geometric interpretation of the OLS regression equation
4 Dummy explanatory variables
4.1 Dummy variables for different intercepts
4.1.1 When there are two categories
4.1.2 When there are more than two categories
4.1.3 Interpretation when the dependent variable is in logarithm
4.1.4 Application: Mitton (2002)
4.1.5 Application: Hakes and Sauer (2006)
4.2 Dummy variables for different slopes
4.2.1 Use of a cross product with a dummy variable
4.2.2 Application: Basu (1997)
4.3 Structural stability of regression models
4.3.1 Test by splitting the sample (Chow test)
4.3.2 Test using dummy variables
4.4 Piecewise linear regression models
4.4.1 Using dummy variables
4.4.2 Using quantitative variables only
4.4.3 Morck, Shleifer and Vishny (1988)
4.5 Summary
Review questions
References
Appendix 4 Dummy variables in EViews and SAS
5 More on multiple regression analysis
5.1 Multicollinearity
5.1.1 Consequences of multicollinearity
5.1.2 Solutions
5.2 Heteroscedasticity
5.2.1 Consequences of heteroscedasticity
5.2.2 Testing for heteroscedasticity
5.2.3 Application: Mitton (2002)
5.3 More on functional form
5.3.1 Quadratic function
5.3.2 Interaction terms
5.4 Applications
5.4.1 Bharadwaj, Tuli and Bonfrer (2011)
5.4.2 Ghosh and Moon (2005)
5.4.3 Arora and Vamvakidis (2005)
5.5 Summary
Review questions
References
Appendix 5 Testing and correcting for heteroscedasticity
6 Endogeneity and two-stage least squares estimation
6.1 Measurement errors
6.1.1 Measurement errors in the dependent variable
6.1.2 Measurement errors in an explanatory variable
6.2 Specification errors
6.2.1 Omitted variables
6.2.2 Inclusion of irrelevant variables
6.2.3 A guideline for model selection
6.3 Two-stage least squares estimation
6.4 Generalized method of moments (GMM)
6.4.1 GMM vs. 2SLS
6.5 Tests for endogeneity
6.5.1 Ramsey (1969) test
6.5.2 Hausman (1978) test
6.6 Applications
6.6.1 Dechow, Sloan and Sweeney (1995)
6.6.2 Beaver, Lambert and Ryan (1987)
6.6.3 Himmelberg and Petersen (1994)
6.7 Summary
Review questions
References
Appendix 6A Estimation of 2SLS and GMM using EViews and SAS
Appendix 6B Hausman test for endogeneity using EViews and SAS
7 Models for panel data
7.1 One big regression
7.2 Fixed effects model
7.2.1 Using time dummies (for bt)
7.2.2 Using cross-section dummies (for a1)
7.2.3 Applying transformations
7.3 Applications
7.3.1 Cornwell and Trumbull (1994)
7.3.2 Blackburn and Neumark (1992)
7.3.3 Garin-Munoz (2006)
7.3.4 Tuli, Bharadwaj and Kohli (2010)
7.4 Random effects
7.5 Fixed vs. random effects models
7.6 Summary
Review questions
References
Appendix 7A Controlling for fixed effects using EViews and SAS
Appendix 7B Is it always possible to control for unit-specific effects?
8 Simultaneous equations models
8.1 Model description
8.2 Estimation methods
8.2.1 Two-stage least squares (2SLS)
8.2.2 Three-stage least squares (3SLS)
8.2.3 Generalized method of moments (GMM)
8.2.4 Full-information maximum likelihood (FIML)
8.3 Identification problem
8.4 Applications
8.4.1 Cornwell and Trumbull (1994)
8.4.2 Beaver, McAnally and Stinson (1997)
8.4.3 Barton (2001)
8.4.4 Datta and Agarwal (2004)
8.5 Summary
Review questions
References
Appendix 8 Estimation of simultaneous equations models using EViews and SAS
9 Vector autoregressive (VAR) models
9.1 VAR models
9.2 Estimation of VAR models
9.3 Granger-causality test
9.4 Forecasting
9.5 Impulse-response analysis
9.6 Variance decomposition analysis
9.7 Applications
9.7.1 Stock and Watson (2001)
9.7.2 Zhang, Fan, Tsai and Wei (2008)
9.7.3 Trusov, Bucklin and Pausels (2009)
9.8 Summary
Review questions
References
Appendix 9 Estimation and analysis of VAR models using SAS
10 Autocorrelation and ARCH/GARCH
10.1 Autocorrelation
10.1.1 Consequences of autocorrelation
10.1.2 Test for autocorrelation
10.1.3 Estimation of autocorrelation
10.2 ARCH-type models
10.2.1 ARCH model
10.2.2 GARCH (Generalized ARCH) model
10.2.3 TGARCH (Threshold GARCH) model
10.2.4 EGARCH (Exponential GARCH) model
10.2.5 GARCH-M model
10.3 Applications
10.3.1 Wang, Salin and Leatham (2002)
10.3.2 Zhang, Fan, Tsai and Wei (2008)
10.3.3 Value at Risk (VaR)
10.4 Summary
Review questions
References
Appendix 10A Test and estimation of autocorrelation using EViews and SAS
Appendix 10B Test and estimation of ARCH/GARCH models using SAS
11 Unit root, cointegration and error correction model
11.1 Spurious regression
11.2 Stationary and nonstationary time series
11.3 Deterministic and stochastic trends
11.4 Unit root tests
11.4.1 Dickey-Fuller (DF) test
11.4.2 Augmented Dickey-Fuller (ADF) test
11.4.3 Example: unit root test using EViews
11.5 Cointegration
11.5.1 Tests for cointegration
11.5.2 Vector error correction models (VECMs)
11.5.3 Example: test and estimation of cointegration using EViews
11.6 Applications
11.6.1 Stock and Watson (1988)
11.6.2 Baillie and Selover (1987)
11.6.3 Granger (1988)
11.6.4 Dritsakis (2004)
11.6.5 Ghosh (1993)
11.7 Summary
Review questions
References
Appendix 11A Unit root test using SAS
Appendix 11B Johansen test for cointegration
Appendix 11C Vector error correction modeling (VECM): test and estimation using SAS
12 Qualitative and limited dependent variable models
12.1 Linear probability model
12.2 Probit model
12.2.1 Interpretation of the coefficients
12.2.2 Measuring the goodness-of-fit
12.3 Logit model
12.3.1 Interpretation of the coefficients
12.3.2 Logit vs. probit
12.3.3 Adjustment for unequal sampling rates: Maddala (1991), Palepu (1986)
12.4 Tobit model
12.4.1 The Tobit model
12.4.2 Applications of the Tobit model
12.4.3 Estimation using EViews and SAS
12.5 Choice-based models
12.5.1 Self-selection model
12.5.2 Choice-based Tobit model
12.5.3 Estimation using SAS
12.6. Applications
12.6.1 Bushee (1998)
12.6.2 Leung, Daouk and Chen (2000)
12.6.3 Shumway (2001)
12.6.4 Robinson and Min (2002)
12.6.5 Leuz and Verrecchia (2000)
12.7 Summary
Review questions
References
Appendix 12 Maximum likelihood estimation (MLE)
Index