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Score your highest in econometrics? Easy. Econometrics can prove challenging for many students unfamiliar with the terms and concepts discussed in a typical econometrics course. Econometrics For Dummies eliminates that confusion with easy-to-understand explanations of important topics in the study of economics. Econometrics For Dummies breaks down this complex subject and provides you with an easy-to-follow course supplement to further refine your understanding of how econometrics works and how it can be applied in real-world situations. An excellent resource for anyone…mehr
Econometrics can prove challenging for many students unfamiliar with the terms and concepts discussed in a typical econometrics course. Econometrics For Dummies eliminates that confusion with easy-to-understand explanations of important topics in the study of economics.
Econometrics For Dummies breaks down this complex subject and provides you with an easy-to-follow course supplement to further refine your understanding of how econometrics works and how it can be applied in real-world situations.
An excellent resource for anyone participating in a college or graduate level econometrics course
Provides you with an easy-to-follow introduction to the techniques and applications of econometrics
Helps you score high on exam day
If you're seeking a degree in economics and looking for a plain-English guide to this often-intimidating course, Econometrics For Dummies has you covered.
Roberto Pedace, PhD, is an associate professor in the Department of Economics at Scripps College. His published work has appeared in Economic Inquiry, Industrial Relations, the Southern Economic Journal, Contemporary Economic Policy, the Journal of Sports Economics, and other outlets.
Inhaltsangabe
Introduction 1
Part I: Getting Started with Econometrics 5
Chapter 1: Econometrics: The Economist’s Approach to Statistical Analysis 7
Chapter 2: Getting the Hang of Probability 21
Chapter 3: Making Inferences and Testing Hypotheses 39
Part II: Building the Classical Linear Regression Model 59
Chapter 4: Understanding the Objectives of Regression Analysis 61
Chapter 5: Going Beyond Ordinary with the Ordinary Least Squares Technique 75
Chapter 6: Assumptions of OLS Estimation and the Gauss-Markov Theorem 93
Chapter 7: The Normality Assumption and Inference with OLS 111
Part III: Working with the Classical Regression Model 135
Chapter 8: Functional Form, Specification, and Structural Stability 137
Chapter 9: Regression with Dummy Explanatory Variables 153
Part IV: Violations of Classical Regression Model Assumptions 173
Chapter 10: Multicollinearity 175
Chapter 11: Heteroskedasticity 191
Chapter 12: Autocorrelation 209
Part V: Discrete and Restricted Dependent Variables in Econometrics 229
Chapter 13: Qualitative Dependent Variables 231
Chapter 14: Limited Dependent Variable Models 253
Part VI: Extending the Basic Econometric Model 265
Chapter 15: Static and Dynamic Models 267
Chapter 16: Diving into Pooled Cross-Section Analysis 281
Chapter 17: Panel Econometrics 291
Part VII: The Part of Tens 305
Chapter 18: Ten Components of a Good Econometrics Research Project 307
Chapter 19: Ten Common Mistakes in Applied Econometrics 315
Appendix: Statistical Tables 321
Index 327
Introduction 1 Part I: Getting Started with Econometrics 5 Chapter 1: Econometrics: The Economist's Approach to Statistical Analysis 7 Chapter 2: Getting the Hang of Probability 21 Chapter 3: Making Inferences and Testing Hypotheses 39 Part II: Building the Classical Linear Regression Model 59 Chapter 4: Understanding the Objectives of Regression Analysis 61 Chapter 5: Going Beyond Ordinary with the Ordinary Least Squares Technique 75 Chapter 6: Assumptions of OLS Estimation and the Gauss-Markov Theorem 93 Chapter 7: The Normality Assumption and Inference with OLS 111 Part III: Working with the Classical Regression Model 135 Chapter 8: Functional Form, Specification, and Structural Stability 137 Chapter 9: Regression with Dummy Explanatory Variables 153 Part IV: Violations of Classical Regression Model Assumptions 173 Chapter 10: Multicollinearity 175 Chapter 11: Heteroskedasticity 191 Chapter 12: Autocorrelation 209 Part V: Discrete and Restricted Dependent Variables in Econometrics 229 Chapter 13: Qualitative Dependent Variables 231 Chapter 14: Limited Dependent Variable Models 253 Part VI: Extending the Basic Econometric Model 265 Chapter 15: Static and Dynamic Models 267 Chapter 16: Diving into Pooled Cross-Section Analysis 281 Chapter 17: Panel Econometrics 291 Part VII: The Part of Tens 305 Chapter 18: Ten Components of a Good Econometrics Research Project 307 Chapter 19: Ten Common Mistakes in Applied Econometrics 315 Appendix: Statistical Tables 321 Index 327
Chapter 1: Econometrics: The Economist’s Approach to Statistical Analysis 7
Chapter 2: Getting the Hang of Probability 21
Chapter 3: Making Inferences and Testing Hypotheses 39
Part II: Building the Classical Linear Regression Model 59
Chapter 4: Understanding the Objectives of Regression Analysis 61
Chapter 5: Going Beyond Ordinary with the Ordinary Least Squares Technique 75
Chapter 6: Assumptions of OLS Estimation and the Gauss-Markov Theorem 93
Chapter 7: The Normality Assumption and Inference with OLS 111
Part III: Working with the Classical Regression Model 135
Chapter 8: Functional Form, Specification, and Structural Stability 137
Chapter 9: Regression with Dummy Explanatory Variables 153
Part IV: Violations of Classical Regression Model Assumptions 173
Chapter 10: Multicollinearity 175
Chapter 11: Heteroskedasticity 191
Chapter 12: Autocorrelation 209
Part V: Discrete and Restricted Dependent Variables in Econometrics 229
Chapter 13: Qualitative Dependent Variables 231
Chapter 14: Limited Dependent Variable Models 253
Part VI: Extending the Basic Econometric Model 265
Chapter 15: Static and Dynamic Models 267
Chapter 16: Diving into Pooled Cross-Section Analysis 281
Chapter 17: Panel Econometrics 291
Part VII: The Part of Tens 305
Chapter 18: Ten Components of a Good Econometrics Research Project 307
Chapter 19: Ten Common Mistakes in Applied Econometrics 315
Appendix: Statistical Tables 321
Index 327
Introduction 1 Part I: Getting Started with Econometrics 5 Chapter 1: Econometrics: The Economist's Approach to Statistical Analysis 7 Chapter 2: Getting the Hang of Probability 21 Chapter 3: Making Inferences and Testing Hypotheses 39 Part II: Building the Classical Linear Regression Model 59 Chapter 4: Understanding the Objectives of Regression Analysis 61 Chapter 5: Going Beyond Ordinary with the Ordinary Least Squares Technique 75 Chapter 6: Assumptions of OLS Estimation and the Gauss-Markov Theorem 93 Chapter 7: The Normality Assumption and Inference with OLS 111 Part III: Working with the Classical Regression Model 135 Chapter 8: Functional Form, Specification, and Structural Stability 137 Chapter 9: Regression with Dummy Explanatory Variables 153 Part IV: Violations of Classical Regression Model Assumptions 173 Chapter 10: Multicollinearity 175 Chapter 11: Heteroskedasticity 191 Chapter 12: Autocorrelation 209 Part V: Discrete and Restricted Dependent Variables in Econometrics 229 Chapter 13: Qualitative Dependent Variables 231 Chapter 14: Limited Dependent Variable Models 253 Part VI: Extending the Basic Econometric Model 265 Chapter 15: Static and Dynamic Models 267 Chapter 16: Diving into Pooled Cross-Section Analysis 281 Chapter 17: Panel Econometrics 291 Part VII: The Part of Tens 305 Chapter 18: Ten Components of a Good Econometrics Research Project 307 Chapter 19: Ten Common Mistakes in Applied Econometrics 315 Appendix: Statistical Tables 321 Index 327
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