Textbook
Principles of Econometrics, 4th EditionJanuary 2011, ©2012

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Table of 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 GaussMarkov 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 pValue.
3.6 Linear Combinations of Parameters.
3.7 Exercises.
Chapter 4 Prediction, GoodnessofFit, and Modeling Issues.
4.1 Least Squares Prediction.
4.2 Measuring GoodnessofFit.
4.3 Modeling Issues.
4.4 Modeling Issues.
4.4 Polynomial Models.
4.5 LogLinear Models.
4.6 LogLog 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 GoodnessofFit.
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 LogLinear 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 HeteroskedasticityConsistent 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 TimeSeries 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 MomentBased 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 ReducedForm Equations.
11.3 The Failure of Least Squares Estimation,
11.4 The Identification Problem.
11.5 TwoStage Least Squares Estimation.
11.6 An Example of TwoStage Least Squares Estimation.
11.7 Supply and Demand at the Fulton Fish Demand.
11.8 Exercises.
Chapter 12 Regression with TimeSeries 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 TimeVarying Volatility and ARCH Models.
14.1 The ARCH Model.
14.2 TimeVarying 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 HausmanTaylor 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.
Appendix A Mathematical Tools.
Appendix B Probability Concepts.
Appendix C Review of Statistical Inference.
Appendix D.
Index.
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 endofchapter questions and problems in each chapter.
 New Primer of Probably and Statistics.
The Wiley Advantage
 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.
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