Applied econometrics: a practical guide
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London ; NewYork
Routledge
2019
|
| Schriftenreihe: | Routledge advanced texts in economics and finance
31 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xiv, 295 Seiten Diagramme |
| ISBN: | 9780367110321 9780367110338 |
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Datensatz im Suchindex
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| adam_text | CONTENTS List offigures List oftables Preface Acknowledgments 1 Review of estimation and hypothesis tests xi xii xiii XV 1 1.1 The problem 1 1.2 Population and sample 1 1.3 Hypotheses 2 1.4 Test statistic and its sampling distribution 2 1.5 Type l and Type II errors 4 1.6 Significance level 4 1.7 p֊value 4 1.8 Powerful tests 5 1.9 Properties ofestimators 8 1.10 Summary 9 Review questions 10 2 Simple linear regression models 2.1 2.2 2.3 2.4 2.5 Introduction 12 2.1.1 A hypothetical example 12 2.1.2 Population regression line 13 2.1.3 Stochastic specification for individuals 14 Ordinary least squares estimation 14 Coefficient ofdetermination (R2) 17 2.3.1 Definition and interpretation of R2 17 2.3.2 Application ofR2: Morek, Yeung and Yu (2000) 18 2.3.3 Application ofR2: Dechow (1994) 19 Hypothesis test 20 2.4.1 Testing Η0:β,=0 vs. Hx βχΦ 0 20 2.4.2 Testing H„: ßx- c vs. Hx ßx^c (c is a constant) 22 The model 22 2.5.1 Key assumptions 22 2.5.2 Causs-Markov Theorem 26 2.5.3 Consistency of the OLS estimators 26 2.5.4 Remarks on model specification 27 12
vi Contents 2.6 Functionalforms 27 2.6.1 Log-log linear models 28 2.6.2 Log-linear models 30 2.7 Effects ofchanging measurement units and levels 31 2.7.1 Changes of measurement units 31 2.7.2 Changes in the levels 33 2.8 Summary 33 Review questions 34 References 38 Appendix 2 How to use EViews, SAS and R 39 3 Multiple linear regression models 3.1 3.2 The basic model 43 Ordinary least squares estimation 45 3.2.1 Obtaining the OLS estimates 45 3.2.2 Interpretation ofregression coefficients 46 3.3 Estimation bias due to correlated-omitted variables 47 3.4 R2 and the adjusted R2 48 3.4.1 Definition and interpretation of R2 48 3.4.2 Adjusted R2 48 3.5 Hypothesis test 49 3.6 Model selection 50 3.6.1 General-to-simple approach 50 3.6.2 A comment on hypothesis testing 52 3.6.3 Guidelines for model selection 53 3.7 Applications 53 3.7.1 Mitton (2002) 53 3.7.2 McAlister, Srinivasan and Kim (2007) 55 3.7.3 Collins, Pincus and Xie (1999) 56 3.7.4 Angrist andPixchke (2009, pp. 64—68) 57 3.8 Summary 59 Review questions 59 References 64 Appendix ЗА Hypothesis test using EViews and SAS 65 Appendix 3B Geometric interpretation ofthe OLS regression equation 67 4 Dummy explanatory variables 4.1 4.2 4.3 Dummy variables for different intercepts 69 4.1.1 When there are two categories 69 4.1.2 When there are more than two categories 72 4.1.3 Interpretation when the dependent variable is in logarithm 72 4.1.4 Application: Mitton (2002) 73 4.1.5 Application: Hakes and Sauer (2006) 74 Dummy variables for different slopes 77 4.2.1 Use of a cross product with a dummy variable 77 4.2.2 Application: Basu
(1997) 79 Structural stability ofregression models 80 4.3.1 Test by splitting the sample (Chow test) 80 4.3.2 Test using dummy variables 80
Contents vii 4.4 Piecewise linear regression models 81 4.4.1 Using dummy variables 81 4.4.2 Using quantitative variables only 81 4.4.3 Morek, Shleifer and Vishny (1988) 82 4.5 Summary 83 Review questions 83 References 86 Appenda 4 Dummy variables in EViews and SAS 87 5 More on multiple regression analysis 89 5.1 Multicollinearity 89 5.1.1 Consequences ofmulticollinearity 91 5.1.2 Solutions 91 5.2 Heteroscedasticity 92 5.2.1 Consequences ofheteroscedasticity 92 5.2.2 Testingfor heteroscedasticity 92 5.2.3 Application: Mitton (2002) 93 5.3 More on functional form 94 5.3.1 Quadratic function 94 5.3.2 Interaction terms 94 5.4 Applications 96 5.4.1 Bharadwaj, Tuli and Bonfrer (2011) 96 5.4.2 Ghosh and Moon (2005) 98 5.4.3 Arora and Vamvakidis (2005) 99 5.5 Summary 100 Review questions 100 References 105 Appendix 5 Testing and correcting for heteroscedasticity 106 6 Endogeneity and two-stage least squares estimation 6.1 Measurement errors 110 6.1.1 Measurement errors in the dependent variable 111 6.1.2 Measurement errors in an explanatory variable 111 6.2 Specification errors 113 6.2.1 Omitted variables 113 6.2.2 Inclusion of irrelevant variables ¡14 6.2.3 A guideline for model selection 114 6.3 Two-stage least squares estimation 115 6.4 Generalized method of moments (GMM) 117 6.4.1 GMM vs. 2SLS 118 6.5 Tests for endogeneity 118 6.5.1 Ramsey (1969) test 118 6.5.2 Hausman (1978) test 118 6.6 Applications 119 6.6.1 Dechow, Sloan and Sweeney (1995) 119 6.6.2 Beaver, Lambert and Ryan (1987) 121 6.6.3 Himmelberg and Petersen (1994) 122 6.7 Summary 122 Review questions 123 109
Contents References 127 Appendix 6A Estimation of2SLS and GMM using EViews and SAS 129 Appendix 6B Hausman testfor endogeneity using EViews and SAS 132 7 Models for panel data 135 7.1 7.2 One big regression 135 Fixed effects model 136 7.2.1 Using time dummies (for bt) 136 7.2.2 Using cross-section dummies (for af 137 7.2.3 Applying transformations 137 7.3 Applications 139 7.3.1 Cornwell and Trumbull (1994) 139 7.3.2 Blackburn and Neumark (1992) 141 7.3.3 Garin-Munoz (2006) 142 7.3.4 Tuli, Bharadwaj and Kohli (2010) 142 7.4 Random effects 145 7.5 Fixed vs. random effects models 147 7.6 Summary 147 Review questions 148 References 151 Appendix 7A Controllingfor fixed effects using EViews and SAS 153 Appendix 7B Is it always possible to controlfor unit-specific effects? 155 8 Simultaneous equations models 157 8.1 8.2 Model description 157 Estimation methods 158 8.2.1 Two-stage least squares (2SLS) 158 8.2.2 Three-stage least squares (3SLS) 159 8.2.3 Generalized method ofmoments (GMM) 160 8.2.4 Full-information maximum likelihood (FIML) 160 8.3 Identification problem 160 8.4 Applications 162 8.4.1 Cornwell and Trumbull (1994) 162 8.4.2 Beaver, McAnally and Stinson (1997) 163 8.4.3 Barton (2001) 165 8.4.4 Datta and Agarwal (2004) 165 8.5 Summary 167 Review questions 167 References 169 Appendix 8 Estimation ofsimultaneous equations models using EViews and SAS 171 9 Vector autoregressive (VAR) models 9.1 9.2 9.3 9.4 9.5 9.6 9.7 VAR models 173 Estimation of VAR models 174 Granger-causality test 175 Forecasting 178 Impulse-response analysis 179 Variance decomposition analysis 181
Applications 183 9.7.1 Stock and Watson (2001) 183 173
Contents ix 9.7.2 Zhang, Fan, Tsai and Wei (2ՈՈ8) 186 9.7.3 Trusov, Biteklin and Pausels (2009) 187 9.8 Summary 191 Review questions 191 References 194 Appendix 9 Estimation and analysis of VAR models using SAS 195 10 Autocorrelation and ARCH/GARCH 203 10.1 Autocorrelation 203 10.1.1 Consequences ofautocorrelation 203 10.1.2 Test for autocorrelation 206 10.1.3 Estimation of autocorrelation 208 10.2 ARCH-type models 208 10.2.1 ARCH model 209 10.2.2 GARCH (GeneralizedARCH) model 212 10.2.3 TGARCH (Threshold GARCH) model 214 10.2.4 EGARCH (Exponential GARCH) model 214 10.2.5 GARCH-Mmodel 215 10.3 Applications 215 10.3.1 Wang, Salin andLeatham (2002) 215 10.3.2 Zhang, Fan, Tsai and Wei (2008) 216 10.3.3 Value at Risk (VaR) 218 10.4 Summary 219 Review questions 220 References 222 Appendix 10A Test and estimation of autocorrelation using EVievvs and SAS 223 Appendix 10B Test and estimation ofARCH/GARCH models using SAS 229 11 Unit root, cointegration and error correction model 11.1 11.2 11.3 11.4 Spurious regression 230 Stationary and nonstationary time series 232 Deterministic and stochastic trends 233 Unit root tests 234 11.4.1 Dickey-Fuller (DF) test 234 11.4.2 Augmented Dickey-Fuller (ADF) test 235 11.4.3 Example: unit root test using EViews 235 11.5 Cointegration 237 11.5.1 Tests for cointegration 237 11.5.2 Vector error correction models (VECMs) 237 11.5.3 Example: test and estimation ofcointegration using EViews 238 11.6 Applications 242 11.6.1 Stock and Watson (1988) 242 11.6.2 Baillie andSelover (1987) 243 11.6.3 Granger (1988) 243 11.6.4 Dritsakis (2004) 245 11.6.5
Ghosh (1993) 247 11.7 Summary 247 Review questions 247 References 250 230
x Contents Appendix HA Unit root test using SAS 252 Appendix 1ÌB Johansen test for cointegration 254 Appendix 11C Vector error correction modeling (VECM): test and estimation using SAS 255 12 Qualitative and limited dependent variable models 262 12.1 Linear probability model 262 12.2 Probit model 263 12.2.1 Interpretation of the coefficients 264 12.2.2 Measuring the goodness-of-fit 266 12.3 Logit model 267 12.3.1 Interpretation of the coefficients 267 12.3.2 Logit vs. probit 269 12.3.3 Adjustment for unequal sampling rates: Maddala (1991), Palepu (1986) 269 12.4 Tobit model 270 12.4.1 The Tobit model 270 12.4.2 Applications of the Tobit model 271 12.4.3 Estimation using EViews and SAS 272 12.5 Choice-based models 273 12.5.1 Self-selection model 274 12.5.2 Choice-based Tobit model 276 12.5.3 Estimation using SAS 277 12.6. Applications 279 12.6.1 Bushee (1998) 279 12.6.2 Leung, Daouk and Chen (2000) 280 12.6.3 Shimway (2001) 281 12.6.4 Robinson and Min (2002) 281 12.6.5 Leuz and Verrecchia (2000) 284 12.7 Summary 286 Review questions 286 References 290 Appendix 12 Maximum likelihood estimation (MLE) 291 Index 293
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| indexdate | 2024-07-10T08:30:00Z |
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| language | English |
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| spelling | Min, Chung-ki Verfasser (DE-588)170948129 aut Applied econometrics a practical guide Chung-ki Min London ; NewYork Routledge 2019 xiv, 295 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Routledge advanced texts in economics and finance 31 Includes bibliographical references and index Econometrics Ökonometrie (DE-588)4132280-0 gnd rswk-swf Ökonometrie (DE-588)4132280-0 s DE-604 Erscheint auch als Online-Ausgabe, eBook 978-0-429-02442-9 Routledge advanced texts in economics and finance 31 (DE-604)BV037241432 31 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031289857&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Min, Chung-ki Applied econometrics a practical guide Routledge advanced texts in economics and finance Econometrics Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4132280-0 |
| title | Applied econometrics a practical guide |
| title_auth | Applied econometrics a practical guide |
| title_exact_search | Applied econometrics a practical guide |
| title_full | Applied econometrics a practical guide Chung-ki Min |
| title_fullStr | Applied econometrics a practical guide Chung-ki Min |
| title_full_unstemmed | Applied econometrics a practical guide Chung-ki Min |
| title_short | Applied econometrics |
| title_sort | applied econometrics a practical guide |
| title_sub | a practical guide |
| topic | Econometrics Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Econometrics Ökonometrie |
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