Introduction to Econometrics

✍ Scribed by Christopher Dougherty

• Publisher: Oxford University Press
• Year: 2011
• Tongue: English
• Leaves: 585
• Edition: 4
• Category: Library

✦ Synopsis

Retaining the student-friendly approach of previous editions, Introduction to Econometrics, Fourth Edition, uses clear and simple mathematics notation and step-by step explanations of mathematical proofs to help students thoroughly grasp the subject. Extensive practical exercises
throughout--including fifty exercises on the same dataset--build students' confidence and provide them with hands-on practice in applying techniques.

NEW TO THE FOURTH EDITION:

An expanded review section at the beginning of the book offers a more comprehensive guide to all of the statistical concepts needed to study econometrics

Additional exercises provide students with even more opportunities to put theory into practice

More Monte Carlo simulations help students use visualization to understand the math

New final sections at the end of each chapter contain summaries and non-technical introductions to more advanced topics

An updated and expanded Companion Website contains resources for students and instructors:

For students:

Data sets
Gretl, a free econometrics software application
PowerPoint-based slides with explanations
A study guide

For instructors:

Instructor manuals for the text and data sets that detail the exercises and their solutions
PowerPoint-based slides
* A "Contact the Author" link

✦ Table of Contents

Introduction to Econometrics
Preface
Contents
Introduction
Review
R.l Introduction
R.2 Discrete random variables and expectations
R.3 Continuous random variables
R.4 Population covariance, covariance and variance rules,and correlation
R.5 Samples, the double structure of a variable, and estimators
R.6 Unbiased ness and efficiency
R.7 Estimators of variance, covariance, and correlation
R.8 The normal distribution
R.9 Hypothesis testing
R.l0 Type II error and the power of a test
R.ll ttests
R.12 Confidence intervals
R.13 One-sided tests
R.14 Probability limits and consistency
R.15 Convergence in distribution and central limit theorems
1. Simple Regression Analysis
1.1 The simple linear model
1.2 Least squares regression with one explanatory variable
1.3 Derivation of the regression coefficients
1.4 Interpretation of a regression equation
1.5 Two important results relating to OLS regressions
1.6 Goodness of fit: R2
2. Properties of the Regression
Coefficients and Hypothesis Testing
2.1 Types of data and regression model
2.2 Assumptions for regression models withnonstochastic regressors
2.3 The random components and unbiased ness ofthe OLS regression coefficients
2.4 A Monte Carlo experiment
2.5 Precision of the regression coefficients
2.6 Testing hypotheses relating to the regression coefficients
2.7 The F test of goodness of fit
3. Multiple R
egression Analysis
3.1 Illustration: a model with two explanatory variables
3.2 Derivation and interpretation of the multipleregression coefficients
3.3 Properties of the multiple regression coefficients
3.4 Multicollinearity
3.5 Goodness of fit: R2
3.6 Prediction
4. Nonlinear Models and Transformations of Variables
4.1 Linearity and nonlinearity
4.2 Logarithmic transformations
4.3 Models with quadratic and interactive variables
4.4 Nonlinear regression
5.
Dummy Variables
5.1 Illustration of the use of a dummy variable
5.2 Extension to more than two categories and to multiple sets of dummy variables
5.3 Slope dummy variables
5.4 The Chow test
6. Specification of
Regression
Variables
6.1 Model specification
6.2 The effect of omitting a variable that ought to be included
6.3 The effect of including a variable that ought not to be included
6.4 Proxy variables
6.5 Testing a linear restriction
6.6 Getting the most out of your residuals
7.
Heteroscedasticity
7.1 Heteroscedasticity and its implications
7.2 Detection of heteroscedasticity
7.3 Remedies for heteroscedasticity
8. Stochastic Regressors and Measurement Errors
8.1 Assumptions for models with stochastic regressors
8.2 Finite sample properties of the OLS regression estimators
8.3 Asymptotic properties of the OLS regression estimators
8.4 The consequences of measurement errors
8.5 Instrumental variables
9. Simultaneous Equations
Estimation
9.1 Simultaneous equations models: structural and reduced form equations
9.2 Simultaneous equations bias
9.3 Instrumental variables estimation
10. Binary Choice and Limited Dependent Variable Models, and Maximum likelihood Estimation
10.1 The linear probability model
10.2 Logit analysis
10.3 Probit analysis
10.4 Censored regressions: tobit analysis
10.5 Sample selection bias
10.6 An introduction to maximum likelihood estimation
Appendix 10.1 Comparing linear and logarithmic specifications
11. Models Using Time Series Data
11.1 Assumptions for regressions with time series data
11.2 Static models
11.3 Models with lagged explanatory variables
11.4 Models with a lagged dependent variable
11.5 Assumption C. 7 and the properties of estimators in autoregressive models
11.6 Simultaneous equations models
11.7 Alternative dynamic representations of time series processes
12. Autocorrelation
12.1 Definition and consequences of autocorrelation
12.2 Detection of autocorrelation
12.3 Fitting a model subject to AR(l) autocorrelation
12.4 Apparent autocorrelation
12.5 Model specification: specific-to-general versus general-to-specific
APPENDIX 12.1 DEMONSTRATION THAT THE DURBIN-WATSON STATISTIC APPROXIMATES 2-2p IN LARGE SAMPLES
13. Introduction to
Nonstationary
Time Series
13.1 Stationarity and nonstationarity
13.2 Spurious regressions
13.3 Graphical techniques for detecting nonstationarity
13.4 Tests of nonstationarity
13.5 Cointegration
13.6 Fitting models with nonstationary time series
14. Introduction to Panel Data Models
14.1 Introduction
14.2 Fixed effects regressions
14.3 Random effects regressions
Appendix A: Statistical tables
Table A.1 Cumulative standardized normal distribution
Table A.2. t distribution
Table A.3. F distribution
Table A.4. chi-squared distribution
Table A.5. Durbin- Watson d statistic
Table A.6. Dickey-Fuller unit root test
Table A.7. Dickey-Fuller unit root test
APPENDIX B: Data Sets
Bibliography
Author
Index
Subject Index