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Applied Econometrics: A Practical Guide [Pehme köide]

4.50/5 (4 hinnangut Goodreads-ist)
(Hankuk University of Foreign Studies, South Korea)
  • Formaat: Paperback / softback, 312 pages, kõrgus x laius: 246x174 mm, kaal: 517 g, 113 Tables, black and white; 50 Line drawings, black and white; 50 Illustrations, black and white
  • Sari: Routledge Advanced Texts in Economics and Finance
  • Ilmumisaeg: 26-Feb-2019
  • Kirjastus: Routledge
  • ISBN-10: 0367110334
  • ISBN-13: 9780367110338
Teised raamatud teemal:
Applied Econometrics: A Practical GuideSuurem pilt
  • Formaat: Paperback / softback, 312 pages, kõrgus x laius: 246x174 mm, kaal: 517 g, 113 Tables, black and white; 50 Line drawings, black and white; 50 Illustrations, black and white
  • Sari: Routledge Advanced Texts in Economics and Finance
  • Ilmumisaeg: 26-Feb-2019
  • Kirjastus: Routledge
  • ISBN-10: 0367110334
  • ISBN-13: 9780367110338
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367110338.html
Märksõnad:

Applied Econometrics: A Practical Guide is an extremely user-friendly and application-focused book on econometrics. Unlike many econometrics textbooks which are heavily theoretical on abstractions, this book is perfect for beginners and promises simplicity and practicality to the understanding of econometric models. Written in an easy-to-read manner, the book begins with hypothesis testing and moves forth to simple and multiple regression models. It also includes advanced topics:

  • Endogeneity and Two-stage Least Squares
  • Simultaneous Equations Models
  • Panel Data Models
  • Qualitative and Limited Dependent Variable Models
  • Vector Autoregressive (VAR) Models
  • Autocorrelation and ARCH/GARCH Models
  • Unit Root and Cointegration

The book also illustrates the use of computer software (EViews, SAS and R) for economic estimating and modeling. Its practical applications make the book an instrumental, go-to guide for solid foundation in the fundamentals of econometrics. In addition, this book includes excepts from relevant articles published in top-tier academic journals. This integration of published articles helps the readers to understand how econometric models are applied to real-world use cases.

Arvustused

'This book provides an intuitive and easy-to-access introduction to econometrics. A variety of topics of econometrics suitable for undergraduate students are discussed in the book along with stimulating examples and computer codes. This book certainly deserves a high recommendation for undergraduate students.' Professor In Choi, Department of Economics, Sogang University, Korea

'The book provides a clear introduction to applied econometrics and is recommended for someone who wants a grounding in basic econometrics. Prof Min is to be congratulated on the simple but effective way in which he presents his ideas.' Ferdinand A. Gul, Alfred Deakin Professor, Department of Accounting, Faculty of Business and Law, Deakin University, Australia 'This book provides an intuitive and easy-to-access introduction to econometrics. A variety of topics of econometrics suitable for undergraduate students are discussed in the book along with stimulating examples and computer codes. This book certainly deserves a high recommendation for undergraduate students.' Professor In Choi, Department of Economics, Sogang University, Korea

'The book provides a clear introduction to applied econometrics and is recommended for someone who wants a grounding in basic econometrics. Prof Min is to be congratulated on the simple but effective way in which he presents his ideas.' Ferdinand A. Gul, Alfred Deakin Professor, Department of Accounting, Faculty of Business and Law, Deakin University

List of figures
xi
List of tables
xii
Preface xiii
Acknowledgments xv
1 Review of estimation and hypothesis tests
1(11)
1.1 The problem
1(1)
1.2 Population and sample
1(1)
1.3 Hypotheses
2(1)
1.4 Test statistic and its sampling distribution
2(2)
1.5 Type I and Type II errors
4(1)
1.6 Significance level
4(1)
1.7 p-value
4(1)
1.8 Powerful tests
5(3)
1.9 Properties of estimators
8(1)
1.10 Summary
9(1)
Review questions
10(2)
2 Simple linear regression models
12(31)
2.1 Introduction
12(1)
2.1 J A hypothetical example
12(2)
2.1.2 Population regression line
13(1)
2.1.3 Stochastic specification for individuals
14(1)
2.2 Ordinary least squares estimation
14(3)
2.3 Coefficient of determination (R1)
17(3)
2.3.1 Definition and interpretation of R
17(1)
2.3.2 Application of R2: Morck, Yeung and Yu (2000)
18(1)
2.3.3 Application of R2: Dechow (1994)
19(1)
2.4 Hypothesis test
20(2)
2.4.1 Testing H0: β1, = 0 vs. H1: β1 ≠ 0
20(2)
2.4.2 Testing H0: β1=c vs. H1: β1 ≠ c (c is a constant)
22(1)
2.5 The model
22(5)
2.5.1 Key assumptions
22(4)
2.5.2 Gauss-Markov Theorem
26(1)
2.5.3 Consistency of the OLS estimators
26(1)
2.5.4 Remarks on model specification
27(1)
2.6 Functional forms
27(4)
2.6.1 Log-log linear models
28(2)
2.6.2 Log-linear models
30(1)
2.7 Effects of changing measurement units and levels
31(2)
2.7.1 Changes of measurement units
31(2)
2.7.2 Changes in the levels
33(1)
2.8 Summary
33(1)
Review questions
34(4)
References
38(1)
Appendix 2 How to use EViews, SAS and R
39(4)
3 Multiple linear regression models
43(26)
3.1 The basic model
43(2)
3.2 Ordinary least squares estimation
45(2)
3.2.1 Obtaining the OLS estimates
45(1)
3.2.2 Interpretation of regression coefficients
46(1)
3.3 Estimation bias due to correlated-omitted variables
47(1)
3.4 R2 and the adjusted R2
48(1)
3.4.1 Definition and interpretation of R2
48(1)
3.4.2 Adjusted R2
48(1)
3.5 Hypothesis test
49(1)
3.6 Model selection
50(3)
3.6.1 General-to-simple approach
50(2)
3.6.2 A comment on hypothesis testing
52(1)
3.6.3 Guidelines for model selection
53(1)
3.7 Applications
53(6)
3.7.1 Mitton(2002)
53(2)
3.7.2 McAlister, Srinivasan and Kim (2007)
55(1)
3.7.3 Collins, Pincus and Xie (1999)
56(1)
3.7.4 Angrist and Pixchke (2009, pp. 64-68)
57(2)
3.8 Summary
59(1)
Review questions
59(5)
References
64(1)
Appendix 3A Hypothesis test using EViews and SAS
65(2)
Appendix 3B Geometric interpretation of the OLS regression equation
67(2)
4 Dummy explanatory variables
69(20)
4.1 Dummy variables for different intercepts
69(8)
4.1.1 When there are two categories
69(3)
4.1.2 When there are more than two categories
72(1)
4.1.3 Interpretation when the dependent variable is in logarithm
72(1)
4.1.4 Application: Milton (2002)
73(1)
4.1.5 Application: Hakes and Sauer (2006)
74(3)
4.2 Dummy variables for different slopes
77(3)
4.2.1 Use of a cross product with a dummy variable
77(2)
4.2.2 Application: Basu (1997)
79(1)
4.3 Structural stability of regression models
80(1)
4.3.1 Test by splitting the sample (Chow test)
80(1)
4.3.2 Test using dummy variables
80(1)
4.4 Piecewise linear regression models
81(1)
4.4.1 Using dummy variables
81(1)
4.4.2 Using quantitative variables only
81(1)
4.4.3 Morck, Shlei/er and Vishny (1988)
82(1)
4.5 Summary
83(1)
Review questions
83(3)
References
86(1)
Appendix 4 Dummy variables in EViews and SAS
87(2)
5 More on multiple regression analysis
89(20)
5.1 Multicollinearily
89(3)
5.1.1 Consequences of multicollinearity
91(1)
5.1.2 Solutions
91(1)
5.2 Heteroscedasticity
92(2)
5.2.1 Consequences of heteroscedasticity
92(1)
5.2.2 Testing for heteroscedasticity
92(1)
5.2.3 Application: Milton (2002)
93(1)
5.3 More on functional form
94(2)
5.3.1 Quadratic function
94(1)
5.3.2 Interaction lerms
94(2)
5.4 Applications
96(4)
5.4.1 Bharadwaj, Tuli and Bonfrer (2011)
96(2)
5.4.2 Ghosh and Moon (2005)
98(1)
5.4.3 Arora and Vamvakidis (2005)
99(1)
5.5 Summary
100(1)
Review questions
100(5)
References
105(1)
Appendix
105(1)
Testing and correcting for heteroscedasticity
106(3)
6 Endogeneity and two-stage least squares estimation
109(1)
6.1 Measurement errors
110(1)
6.1.1 Measurement errors in the dependent variable
111(1)
6.1.2 Measurement errors in an explanatory variable
111(2)
6.2 Specification errors
113(1)
6.2.1 Omitted variables
113(1)
6.2.2 Inclusion of irrelevant variables
114(1)
6.2.3 A guideline for model selection
114(1)
6.3 Two-stage least squares estimation
115(2)
6.4 Generalized method of moments (GMM)
117(1)
6.4.1 GMM vs. 2SLS
118(1)
6.5 Tests for endogeneity
118(1)
6.5.1 Ramsey (1969) test
118(1)
6.5.2 Hausman (1978) test
118(1)
6.6 Applications
119(1)
6.6.1 Dechow, Sloan and Sweeney (1995)
119(2)
6.6.2 Beaver, Lambert and Ryan (1987)
121(1)
6.6.3 Himmelberg and Petersen (1994)
122(1)
6.7 Summary
122(1)
Review questions
123(4)
References
127(2)
Appendix 6A Estimation of2SLS arid GMM using EViews and SAS
129(3)
Appendix 6B Hausman test for endogeneity using EViews and SAS
132(3)
7 Models for panel data
135(22)
7.1 One big regression
135(1)
7.2 Fixed effects model
136(3)
7.2.1 Using time dummies (for bj
136(1)
7.2.2 Using cross-section dummies (for aj
137(1)
7.2.3 Applying transformations
137(2)
7.3 Applications
139(6)
7.3.1 Cornwall and Trumbull (1994)
139(2)
7.3.2 Blackburn and Neumark(1992)
141(1)
7.3.3 Garin-Munoz (2006)
142(1)
7.3.4 Tub, Bharadwaj and Kohli (2010)
142(3)
7.4 Random effects
145(2)
7.5 Fixed vs. random effects models
147(1)
7.6 Summary
147(1)
Review questions
148(3)
References
151(2)
Appendix 7A Controlling for fixed effects using EViews and SAS
153(2)
Appendix 7B Is it always possible to control for unit-specific effects?
155(2)
8 Simultaneous equations models
157(16)
8.1 Model description
157(1)
8.2 Estimation methods
158(2)
8.2.1 Two-stage least squares (2SLS)
158(1)
8.2.2 Three-stage least squares (3SLS)
159(1)
8.2.3 Generalized method of moments (GMM)
160(1)
8.2.4 Full-information maximum likelihood (FIML)
160(1)
8.3 Identification problem
160(2)
8.4 Applications
162(5)
8.4.1 Cornwell and Trumbull (1994)
162(1)
8.4.2 Beaver. McAnally unci Stinson (1997)
163(2)
8.4.3 Barton (2001)
165(1)
8.4.4 Datta and Agarwal (2004)
165(2)
8.5 Summary
167(1)
Review questions
167(2)
References
169(2)
Appendix 8 Estimation of simultaneous equations models using EViews and SAS
171(2)
9 Vector autoregressive (VAR) models
173(1)
9.1 VAR models
173(1)
9.2 Estimation of VAR models
174(1)
9.3 Granger-causality test
175(3)
9.4 Forecasting
178(1)
9.5 Impulse-response analysis
179(2)
9.6 Variance decomposition analysis
181(2)
9.7 Applications
183(1)
9.7.1 Stock and Watson (2001)
183(3)
9.7.2 Zhang, Fan, Tsai and Wei (21)08)
186(1)
9.7.3 Trusov, Buck/in and Pauscls (2009)
187(4)
9.8 Summary
191(1)
Review questions
191(3)
References
194(1)
Appendix 9 Estimation and analysis of VAR models using SAS
195(8)
10 Autocorrelation and ARCH/GARCH
203(1)
10.1 Autocorrelation
203(5)
10.1.1 Consequences of autocorrelation
203(3)
10.1.2 Test for autocorrelation
206(2)
10.1.3 Estimation of autocorrelation
208(1)
10.2 ARCH-type models
208(1)
10.2.1 ARCH model
209(3)
10.2.2 GARCH (Generalized ARCH) model
212(2)
10.2.3 TGARCH (Threshold GARCH) model
214(1)
10.2.4 EGARCH (Exponential GARCH) model
214(1)
10.2.5 GARCH-M model
215(1)
10.3 Applications
215(4)
10.3.1 Wang, Sal in and Leal ham (2002)
215(1)
10.3.2 Zhang, Fan, Tsai and Wei (2008)
216(2)
10.3.3 Value at Risk (VaR)
218(1)
10.4 Summary
219(1)
Review questions
220(2)
References
222(1)
Appendix 11A Test and estimation of autocorrelation using EViews and SAS
223(6)
Appendix 11B Test and estimation of ARCH/GARCH models using SAS
229(1)
11 Unit root, cointegration and error correction model
230(1)
11.1 Spurious regression
230(2)
11.2 Stationary and nonstationary time series
232(1)
11.3 Deterministic and stochastic trends
233(1)
11.4 Unit root tests
234(1)
11.4.1 Dickey-Fuller (DF) test
234(1)
11.4.2 Augmented Dickey-Fuller (ADF) test
235(1)
11.4.3 Example: unit root test using EViews
235(2)
11.5 Cointegration
237(5)
11.5.1 Tests for cointegration
237(1)
11.5.2 Vector error correction models (VECMs)
237(1)
11.5.3 Example: test and estimation of cointegration using EViews
238(4)
11.6 Applications
242(1)
11.6.1 Stock and Watson (1988)
242(1)
11.6.2 Baillie and Selover (1987)
243(1)
11.6.3 Granger (1988)
243(2)
11.6.4 Dritsakis (2004)
245(2)
11.6.5 Ghosh (1993)
247(1)
11.7 Summary
247(1)
Review questions
247(3)
References
250(2)
Appendix 11A Unit root test using SAS
252(2)
Appendix 11B Johansen test for cointegration
254(1)
Appendix 11C Vector error correction modeling (VECM): test and estimation using SAS
255(7)
12 Qualitative and limited dependent variable models
262(1)
12.1 Linear probability model
262(1)
12.2 Probit model
263(1)
12.2.1 Interpretation of the coefficients
264(2)
12.2.2 Measuring the goodness-of-fit
266(1)
12.3 Logit model
267(1)
12.3.1 Interpretation of the coefficients
267(2)
12.3.2 Logit vs. probil
269(1)
12.3.3 Adjustment for unequal sampling rates: Maddala (1991), Palepu (1986)
269(1)
12.4 Tobit model
270(1)
12.4.1 The Tobit model
270(1)
12.4.2 Applications of the Tobit model
271(1)
12.4.3 Estimation using EViews and SAS
272(1)
12.5 Choice-based models
273(1)
12.5.1 Self-selection model
274(2)
12.5.2 Choice-based Tobit model
276(1)
12.5.3 Estimation using SAS
277(2)
12.6 Applications
279(1)
12.6.1 Bushee(1998)
279(1)
12.6.2 Leung, Daouk and Chen (2O00)
280(1)
12.6.3 Shwnway (2001)
281(1)
12.6.4 Robinson and Min (2002)
281(3)
12.6.5 Leu: and Verrecchia (2000)
284(2)
12.7 Summary
286(1)
Review questions
286(4)
References
290(1)
Appendix 12 Maximum likelihood estimation (MLE)
291(2)
Index 293
Chung-ki Min is Professor of Economics at Hankuk University of Foreign Studies, Seoul, South Korea.