Principles of Econometrics is an introductory book for undergraduate students in economics and finance, and can be used for MBA and first-year graduate students in many fields. The 4th Edition provides students with an understanding of why econometrics is necessary and a working knowledge of basic econometric tools. This text emphasizes motivation, understanding and implementation by introducing very simple economic models and asking economic questions that students can answer.
Key Features
- Applies basic econometric tools to modeling, estimation, inference, and forecasting through real world problems.
- Teaches students to evaluate critically the results and conclusions from others who use basic econometric tools.
- Provides a foundation and understanding for further study of econometrics.
- Students will gain an appreciation for the ranges of more advanced techniques that exist and may be covered in later econometric courses.
New to this edition
- Thoroughly updated to reflect current state of economic and financial markets.
- Comprehensive revision of Chapter 9: Regression with Time Series Data: Stationary Variables.
- New content on Kernel Density Fitting and Analysis of Treatment Effects.
- New end-of-chapter questions and problems in each chapter.
- New Primer of Probably and Statistics.
Contents
Chapter 1 An Introduction to Econometrics.
- 1.1 Why Study Econometrics?
- 1.2 What Is Econometrics About?
- 1.3 The Econometric Model.
- 1.4 How Are Data Generated?
- 1.5 Economic Data Types.
- 1.6 The Research Process.
- 1.7 Writing An Empirical Research Paper.
- 1.8 Sources of Economic Data.
Probability Primer.
- P.1 Random Variables.
- P.2 Probability Distributions.
- P.3 Joint, Marginal, and Conditional Probabilities.
- P.4 A Digression: Summation Notation.
- P.5 Properties of Probability Distributions.
- P.6 The Normal Distribution.
- P.7 Exercises.
Chapter 2 The Simple Linear Regression Model.
- 2.1 An Economic Model.
- 2.2 An Econometric Model.
- 2.3 Estimating the Regression Parameters.
- 2.4 Assessing the Least Squares Estimators.
- 2.5 The Gauss-Markov Theorem.
- 2.6 The Probability Distributions of the Least Squares Estimators.
- 2.7 Estimating the Variance of the Error Term.
- 2.8 Estimating Nonlinear Relationships.
- 2.9 Regression with Indicator Variables.
- 2.10 Exercises.
Chapter 3 Interval Estimation and Hypothesis Testing.
- 3.1 Interval Estimation.
- 3.2 Hypothesis Tests.
- 3.3 Rejection Regions for Specific Alternatives.
- 3.4 Examples of Hypothesis Tests.
- 3.5 The p-Value.
- 3.6 Linear Combinations of Parameters.
- 3.7 Exercises.
Chapter 4 Prediction, Goodness-of-Fit, and Modeling Issues.
- 4.1 Least Squares Prediction.
- 4.2 Measuring Goodness-of-Fit.
- 4.3 Modeling Issues.
- 4.4 Modeling Issues.
- 4.4 Polynomial Models.
- 4.5 Log-Linear Models.
- 4.6 Log-Log Models.
- 4.7 Exercises.
Chapter 5 The Multiple Regression Model.
- 5.1 Introduction.
- 5.2 Estimating the Parameters of the Multiple Regression Model.
- 5.3 Sampling Properties of the Least Squares Estimator.
- 5.4 Interval Estimation.
- 5.5 Hypothesis Testing.
- 5.6 Polynomial Equations.
- 5.7 Interaction Variables.
- 5.8 Measuring Goodness-of-Fit.
- 5.9 Exercises.
Chapter 6 Further Inference in the Multiple Regression Model.
- 6.1 Testing Joint Hypotheses.
- 6.2 The Use of Nonsample Information.
- 6.3 Model Specification.
- 6.4 Poor Data, Collinearity, and Insignificance.
- 6.5 Prediction.
- 6.6 Exercises.
Chapter 7 Using Indicator Variables.
- 7.1 Indicator Variables.
- 7.2 Applying Indicator Variables.
- 7.3 Log-Linear Models.
- 7.4 The Linear Probability Model.
- 7.5 Treatment Effects.
- 7.6 Exercises.
Chapter 8 Heteroskedasticity.
- 8.1 The Nature of Heteroskedasticity.
- 8.2 Detecting Heteroskedasticity.
- 8.3 Heteroskedasticity-Consistent Standard Errors.
- 8.4 Generalized Least Squares: Known Form of Variance.
- 8.5 Generalized Least Squares: Unknown Form of Variance.
- 8.6 Heteroskedasticity in the Linear Probability Model.
- 8.7 Exercises.
Chapter 9 Regression with Time-Series Data: Stationary Variables.
- 9.1 Introduction.
- 9.2 Finite Distributed Lags.
- 9.3 Serial Correlation.
- 9.4 Other Tests for Serially Correlated Errors.
- 9.5 Estimation with Serially Correlated Errors.
- 9.6 Autoregressive Distributed Lag Models.
- 9.7 Forecasting.
- 9.8 Multiplier Analysis.
- 9.9 Exercises.
Chapter 10 Random Regressors and Moment-Based Estimation.
- 10.1 Linear Regression with Random x's.
- 10.2 Cases in which x and e Are Correlated.
- 10.3 Estimators Based on the Method of Moments.
- 10.4 Specification Tests.
- 10.5 Exercises.
Chapter 11 Simultaneous Equations Models.
- 11.1 A Supply and Demand Model.
- 11.2 The Reduced-Form Equations.
- 11.3 The Failure of Least Squares Estimation,
- 11.4 The Identification Problem.
- 11.5 Two-Stage Least Squares Estimation.
- 11.6 An Example of Two-Stage Least Squares Estimation.
- 11.7 Supply and Demand at the Fulton Fish Demand.
- 11.8 Exercises.
Chapter 12 Regression with Time-Series Data: Nonstationary Variables.
- 12.1 Stationary and Nonstationary Variables.
- 12.2 Spurious Regressions.
- 12.3 Unit Root Tests for Stationarity.
- 12.4 Cointegration.
- 12.5 Regression When There Is No Cointegration.
- 12.6 Exercises.
Chapter 13 Vector Error Correction and Vector Autoregressive Models.
- 13.1 VEC and VAR Models.
- 13.2 Estimating a Vector Error Correction Model.
- 13.3 Estimating a VAR Model.
- 13.4 Impulse Responses and Variance Decompositions.
- 13.5 Exercises.
Chapter 14 Time-Varying Volatility and ARCH Models.
- 14.1 The ARCH Model.
- 14.2 Time-Varying Volatility.
- 14.3 Testing. Estimating, and Forecasting.
- 14.4 Extensions.
- 14.5 Exercises.
Chapter 15 Panel Data Models.
- 15.1 A Microeconomic Panel.
- 15.2 Pooled Model.
- 15.3 The Fixed Effects Model.
- 15.4 The Random Effects Model.
- 15.5 Comparing Fixed and Random Effects Estimators.
- 15.6 The Hausman-Taylor Estimator.
- 15.7 Sets of Regression Equations.
- 15.8 Exercises.
Chapter 16 Qualitative and Limited Dependent Variable Models.
- 16.1 Models with Binary Dependent Variables.
- 16.2 The Logit Model for Binary Choice.
- 16.3 Multinomial Logit.
- 16.4 Conditional Logit.
- 16.5 Ordered Choice Models.
- 16.6 Models for Count Data.
- 16.7 Limited Dependent Variable Models.
- 16.8 Exercises.
Appendices
- Appendix A Mathematical Tools.
- Appendix B Probability Concepts.
- Appendix C Review of Statistical Inference.
- Appendix D.
- Index.
Book Details
- Hardcover: 784 pages
- Publisher: Wiley; 4 edition (January 4, 2011)
- Language: English
- ISBN-10: 0470626739
- ISBN-13: 978-0470626733
- Product Dimensions: 10 x 7.2 x 1.3 inches
- List Price: $136.66