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ISBN-10 ‏ : ‎ 130527010X
ISBN-13 ‏ : ‎ 978-1305270107
Author: Jeffrey M. Wooldridge

This book is a standalone book and will not include any access codes. Discover how empirical researchers today actually consider and apply econometric methods with the practical approach in Wooldridge’s INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 6E. Unlike traditional texts, this book uniquely demonstrates how econometrics has moved beyond a set of abstract tools to become genuinely useful for answering questions in business, policy evaluation, and forecasting. INTRODUCTORY ECONOMETRICS is organized around the type of data being analyzed with a systematic approach that only introduces assumptions as they are needed. This makes the material easier to understand and, ultimately, leads to better econometric practices. Packed with relevant applications, the text incorporates more than 100 intriguing data sets, available in six formats. Updates introduce the latest emerging developments in the field. Gain a full understanding of the impact of econometrics in practice today with the insights and applications found only in INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 6E.

Table of Content:

Ch 1: The Nature of Econometrics and Economic Data
1-1 What is Econometrics?
1-2 Steps in Empirical Economic Analysis
1-3 The Structure of Economic Data
1-4 Causality and the Notion of Ceteris Paribus in Econometric Analysis
Summary
Key Terms
Problems
Computer Exercises
Part 1: Regression Analysis with Cross-Sectional Data
Ch 2: The Simple Regression Model
2-1 Definition of the Simple Regression Model
2-2 Deriving the Ordinary Least Squares Estimates
2-3 Properties of OLS on Any Sample of Data
2-4 Units of Measurement and Functional Form
2-5 Expected Values and Variances of the OLS Estimators
2-6 Regression through the Origin and Regression on a Constant
Summary
Key Terms
Problems
Computer Exercises
Appendix 2A
Ch 3: Multiple Regression Analysis: Estimation
3-1 Motivation for Multiple Regression
3-2 Mechanics and Interpretation of Ordinary Least Squares
3-3 The Expected Value of the OLS Estimators
3-4 The Variance of the OLS Estimators
3-5 Efficiency of OLS: The Gauss-Markov Theorem
3-6 Some Comments on the Language of Multiple Regression Analysis
Summary
Key Terms
Problems
Computer Exercises
Appendix 3A
Ch 4: Multiple Regression Analysis: Inference
4-1 Sampling Distributions of the OLS Estimators
4-2 Testing Hypotheses about a Single Population Parameter: The t Test
4-3 Confidence Intervals
4-4 Testing Hypotheses about a Single Linear Combination of the Parameters
4-5 Testing Multiple Linear Restrictions: The F Test
4-6 Reporting Regression Results
Summary
Key Terms
Problems
Computer Exercises
Ch 5: Multiple Regression Analysis: OLS Asymptotics
5-1 Consistency
5-2 Asymptotic Normality and Large Sample Inference
5-3 Asymptotic Efficiency of OLS
Summary
Key Terms
Problems
Computer Exercises
Appendix 5A
Ch 6: Multiple Regression Analysis: Further Issues
6-1 Effects of Data Scaling on OLS Statistics
6-2 More on Functional Form
6-3 More on Goodness-of-Fit and Selection of Regressors
6-4 Prediction and Residual Analysis
Summary
Key Terms
Problems
Computer Exercises
Appendix 6A
Ch 7: Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables
7-1 Describing Qualitative Information
7-2 A Single Dummy Independent Variable
7-3 Using Dummy Variables for Multiple Categories
7-4 Interactions Involving Dummy Variables
7-5 A Binary Dependent Variable: The Linear Probability Model
7-6 More on Policy Analysis and Program Evaluation
7-7 Interpreting Regression Results with Discrete Dependent Variables
Summary
Key Terms
Problems
Computer Exercises
Ch 8: Heteroskedasticity
8-1 Consequences of Heteroskedasticity for OLS
8-2 Heteroskedasticity-Robust Inference after OLS Estimation
8-3 Testing for Heteroskedasticity
8-4 Weighted Least Squares Estimation
8-5 The Linear Probability Model Revisited
Summary
Key Terms
Problems
Computer Exercises
Ch 9: More on Specification and Data Issues
9-1 Functional Form Misspecification
9-2 Using Proxy Variables for Unobserved Explanatory Variables
9-3 Models with Random Slopes
9-4 Properties of OLS under Measurement Error
9-5 Missing Data, Nonrandom Samples, and Outlying Observations
9-6 Least Absolute Deviations Estimation
Summary
Key Terms
Problems
Computer Exercises
Part 2: Regression Analysis with Time Series Data
Ch 10: Basic Regression Analysis with Time Series Data
10-1 The Nature of Time Series Data
10-2 Examples of Time Series Regression Models
10-3 Finite Sample Properties of OLS under Classical Assumptions
10-4 Functional Form, Dummy Variables, and Index Numbers
10-5 Trends and Seasonality
Summary
Key Terms
Problems
Computer Exercises
Ch 11: Further Issues in Using OLS with Time Series Data
11-1 Stationary and Weakly Dependent Time Series
11-2 Asymptotic Properties of OLS
11-3 Using Highly Persistent Time Series in Regression Analysis
11-4 Dynamically Complete Models and the Absence of Serial Correlation
11-5 The Homoskedasticity Assumption for Time Series Models
Summary
Key Terms
Problems
Computer Exercises
Ch 12: Serial Correlation and Heteroskedasticity in Time Series Regressions
12-1 Properties of OLS with Serially Correlated Errors
12-2 Testing for Serial Correlation
12-3 Correcting for Serial Correlation with Strictly Exogenous Regressors
12-4 Differencing and Serial Correlation
12-5 Serial Correlation-Robust Inference after OLS
12-6 Heteroskedasticity in Time Series Regressions
Summary
Key Terms
Problems
Computer Exercises
Part 3: Advanced Topics
Ch 13: Pooling Cross Sections across Time: Simple Panel Data Methods
13-1 Pooling Independent Cross Sections across Time
13-2 Policy Analysis with Pooled Cross Sections
13-3 Two-Period Panel Data Analysis
13-4 Policy Analysis with Two-Period Panel Data
13-5 Differencing with More Than Two Time Periods
Summary
Key Terms
Problems
Computer Exercises
Appendix 13A
Ch 14: Advanced Panel Data Methods
14-1 Fixed Effects Estimation
14-2 Random Effects Models
14-3 The Correlated Random Effects Approach
14-4 Applying Panel Data Methods to Other Data Structures
Summary
Key Terms
Problems
Computer Exercises
Appendix 14A
Ch 15: Instrumental Variables Estimation and Two Stage Least Squares
15-1 Motivation: Omitted Variables in a Simple Regression Model
15-2 IV Estimation of the Multiple Regression Model
15-3 Two Stage Least Squares
15-4 IV Solutions to Errors-in-Variables Problems
15-5 Testing for Endogeneity and Testing Overidentifying Restrictions
15-6 2SLS with Heteroskedasticity
15-7 Applying 2SLS to Time Series Equations
15-8 Applying 2SLS to Pooled Cross Sections and Panel Data
Summary
Key Terms
Problems
Computer Exercises
Appendix 15A
Ch 16: Simultaneous Equations Models
16-1 The Nature of Simultaneous Equations Models
16-2 Simultaneity Bias in OLS
16-3 Identifying and Estimating a Structural Equation
16-4 Systems with More Than Two Equations
16-5 Simultaneous Equations Models with Time Series
16-6 Simultaneous Equations Models with Panel Data
Summary
Key Terms
Problems
Computer Exercises
Ch 17: Limited Dependent Variable Models and Sample Selection Corrections
17-1 Logit and Probit Models for Binary Response
17-2 The Tobit Model for Corner Solution Responses
17-3 The Poisson Regression Model
17-4 Censored and Truncated Regression Models
17-5 Sample Selection Corrections
Summary
Key Terms
Problems
Computer Exercises
Appendix 17A
Appendix 17B
Ch 18: Advanced Time Series Topics
18-1 Infinite Distributed Lag Models
18-2 Testing for Unit Roots
18-3 Spurious Regression
18-4 Cointegration and Error Correction Models
18-5 Forecasting
Summary
Key Terms
Problems
Computer Exercises
Ch 19: Carrying out an Empirical Project
19-1 Posing a Question
19-2 Literature Review
19-3 Data Collection
19-4 Econometric Analysis
19-5 Writing an Empirical Paper
Summary
Key Terms
Sample Empirical Projects
List of Journals
Data Sources
Appendix A: Basic Mathematical Tools
Appendix B: Fundamentals of Probability
Appendix C: Fundamentals of Mathematical Statistics
Appendix D: Summary of Matrix Algebra
Appendix E: The Linear Regression Model in Matrix Form
Appendix F: Answers to Chapter Questions
Appendix G: Statistical Tables
References
Glossary
Index

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