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Download Introductory Econometrics: A Modern Approach (7th Edition) written by Jeffrey M. Wooldridge in PDF format. This book is under the category Economics and bearing the isbn/isbn13 number 1337558869/9781337558860. You may reffer the table below for additional details of the book.

## Description

Gain an understanding of how econometrics can answer today’s questions in policy evaluation; business; and forecasting with *Wooldridge’s Introductory Econometrics: A Modern Approach; 7th edition (PDF)*. Unlike traditional textbooks; this ebook’s professional; yet practical; approach demonstrates how econometrics has moved beyond a set of abstract tools to become genuinely useful for answering questions across a variety of disciplines. The author

*James Wooldridge*has organized the ebook’s presentation 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 textbook incorporates more than 100 data sets in different formats. Updates introduce the latest developments in the field; including the recent advances in the so-called ‘causal effects’ or ‘treatment effects;’ to provide a complete understanding of the impact and importance of econometrics today.

*P.S We also have Introductory Econometrics: A Modern Approach 7e testbank and other instructor resources for sale.*

**NOTE: This product only contains the ebook Introductory Econometrics: A Modern Approach; 7th edition in PDF. No access codes included.**

## Additional information

book-author | Jeffrey M. Wooldridge |
---|---|

publisher | Cengage Learning; 7th edition |

file-type | |

pages | 816 pages |

language | English |

isbn10 | 1337558869 |

isbn13 | 9781337558860 |

## Table of contents

Table of contents :

Brief Contents

Contents

Chapter 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

1-3a Cross-Sectional Data

1-3b Time Series Data

1-3c Pooled Cross Sections

1-3d Panel or Longitudinal Data

1-3e A Comment on Data Structures

1-4 Causality, Ceteris Paribus, and Counterfactual Reasoning

Summary

Key Terms

Problems

Computer Exercises

Part 1: Regression Analysis with Cross-Sectional Data

Chapter 2: The Simple Regression Model

2-1 Definition of the Simple Regression Model

2-2 Deriving the Ordinary Least Squares Estimates

2-2a A Note on Terminology

2-3 Properties of OLS on Any Sample of Data

2-3a Fitted Values and Residuals

2-3b Algebraic Properties of OLS Statistics

2-3c Goodness-of-Fit

2-4 Units of Measurement and Functional Form

2-4a The Effects of Changing Units of Measurement on OLS Statistics

2-4b Incorporating Nonlinearities in Simple Regression

2-4c The Meaning of “Linear” Regression

2-5 Expected Values and Variances of the OLS Estimators

2-5a Unbiasedness of OLS

2-5b Variances of the OLS Estimators

2-5c Estimating the Error Variance

2-6 Regression through the Origin and Regression on a Constant

2-7 Regression on a Binary Explanatory Variable

2-7a Counterfactual Outcomes, Causality, and Policy Analysis

Summary

Key Terms

Problems

Computer Exercises

Chapter 3: Multiple Regression Analysis: Estimation

3-1 Motivation for Multiple Regression

3-1a The Model with Two Independent Variables

3-1b The Model with k Independent Variables

3-2 Mechanics and Interpretation of Ordinary Least Squares

3-2a Obtaining the OLS Estimates

3-2b Interpreting the OLS Regression Equation

3-2c On the Meaning of “Holding Other Factors Fixed” in Multiple Regression

3-2d Changing More Than One Independent Variable Simultaneously

3-2e OLS Fitted Values and Residuals

3-2f A “Partialling Out” Interpretation of Multiple Regression

3-2g Comparison of Simple and Multiple Regression Estimates

3-2h Goodness-of-Fit

3-2i Regression through the Origin

3-3 The Expected Value of the OLS Estimators

3-3a Including Irrelevant Variables in a Regression Model

3-3b Omitted Variable Bias: The Simple Case

3-3c Omitted Variable Bias: More General Cases

3-4 The Variance of the OLS Estimators

3-4a The Components of the OLS Variances: Multicollinearity

3-4b Variances in Misspecified Models

3-4c Estimating s2: Standard Errors of the OLS Estimators

3-5 Efficiency of OLS: The Gauss-Markov Theorem

3-6 Some Comments on the Language of Multiple Regression Analysis

3-7 Several Scenarios for Applying Multiple Regression

3-7a Prediction

3-7b Efficient Markets

3-7c Measuring the Tradeoff between Two Variables

3-7d Testing for Ceteris Paribus Group Differences

3-7e Potential Outcomes, Treatment Effects, and Policy Analysis

Summary

Key Terms

Problems

Computer Exercises

Chapter 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

4-2a Testing against One-Sided Alternatives

4-2b Two-Sided Alternatives

4-2c Testing Other Hypotheses about bj

4-2d Computing p-Values for t Tests

4-2e A Reminder on the Language of Classical Hypothesis Testing

4-2f Economic, or Practical, versus Statistical Significance

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

4-5a Testing Exclusion Restrictions

4-5b Relationship between F and t Statistics

4-5c The R-Squared Form of the F Statistic

4-5d Computing p-values for F Tests

4-5e The F Statistic for Overall Significance of a Regression

4-5f Testing General Linear Restrictions

4-6 Reporting Regression Results

4-7 Revisiting Causal Effects and Policy Analysis

Summary

Key Terms

Problems

Computer Exercises

Chapter 5: Multiple Regression Analysis: OLS Asymptotics

5-1 Consistency

5-1a Deriving the Inconsistency in OLS

5-2 Asymptotic Normality and Large Sample Inference

5-2a Other Large Sample Tests: The Lagrange Multiplier Statistic

5-3 Asymptotic Efficiency of OLS

Summary

Key Terms

Problems

Computer Exercises

Chapter 6: Multiple Regression Analysis: Further Issues

6-1 Effects of Data Scaling on OLS Statistics

6-1a Beta Coefficients

6-2 More on Functional Form

6-2a More on Using Logarithmic Functional Forms

6-2b Models with Quadratics

6-2c Models with Interaction Terms

6-2d Computing Average Partial Effects

6-3 More on Goodness-of-Fit and Selection of Regressors

6-3a Adjusted R-Squared

6-3b Using Adjusted R-Squared to Choose between Nonnested Models

6-3c Controlling for Too Many Factors in Regression Analysis

6-3d Adding Regressors to Reduce the Error Variance

6-4 Prediction and Residual Analysis

6.4 a Confidence Intervals for Predictions

6-4b Residual Analysis

6-4c Predicting y When log(y) Is the Dependent Variable

6-4d Predicting y When the Dependent Variable Is log(y)

Summary

Key Terms

Problems

Computer Exercises

Chapter 7: Multiple Regression Analysis with Qualitative Information

7-1 Describing Qualitative Information

7-2 A Single Dummy Independent Variable

7-2a Interpreting Coefficients on Dummy Explanatory Variables When the Dependent Variable Is log(y)

7-3 Using Dummy Variables for Multiple Categories

7-3a Incorporating Ordinal Information by Using Dummy Variables

7-4 Interactions Involving Dummy Variables

7-4a Interactions among Dummy Variables

7-4b Allowing for Different Slopes

7-4c Testing for Differences in Regression Functions across Groups

7-5 A Binary Dependent Variable: The Linear Probability Model

7-6 More on Policy Analysis and Program Evaluation

7-6a Program Evaluation and Unrestricted Regression Adjustment

7-7 Interpreting Regression Results with Discrete Dependent Variables

Summary

Key Terms

Problems

Computer Exercises

Chapter 8: Heteroskedasticity

8-1 Consequences of Heteroskedasticity for OLS

8-2 Heteroskedasticity-Robust Inference after OLS Estimation

8-2a Computing Heteroskedasticity-Robust LM Tests

8-3 Testing for Heteroskedasticity

8-3a The White Test for Heteroskedasticity

8-4 Weighted Least Squares Estimation

8-4a The Heteroskedasticity Is Known up to a Multiplicative Constant

8-4b The Heteroskedasticity Function Must Be Estimated: Feasible GLS

8-4c What If the Assumed Heteroskedasticity Function Is Wrong?

8-4d Prediction and Prediction Intervals with Heteroskedasticity

8-5 The Linear Probability Model Revisited

Summary

Key Terms

Problems

Computer Exercises

Chapter 9: More on Specification and Data Issues

9-1 Functional Form Misspecification

9-1a RESET as a General Test for Functional Form Misspecification

9-1b Tests against Nonnested Alternatives

9-2 Using Proxy Variables for Unobserved Explanatory Variables

9-2a Using Lagged Dependent Variables as Proxy Variables

9-2b A Different Slant on Multiple Regression

9-2c Potential Outcomes and Proxy Variables

9-3 Models with Random Slopes

9-4 Properties of OLS under Measurement Error

9-4a Measurement Error in the Dependent Variable

9-4b Measurement Error in an Explanatory Variable

9-5 Missing Data, Nonrandom Samples, and Outlying Observations

9-5a Missing Data

9-5b Nonrandom Samples

9-5c Outliers and Influential Observations

9-6 Least Absolute Deviations Estimation

Summary

Key Terms

Problems

Computer Exercises

Part 2: Regression Analysis with Time Series Data

Chapter 10: Basic Regression Analysis with Time Series Data

10-1 The Nature of Time Series Data

10-2 Examples of Time Series Regression Models

10-2a Static Models

10-2b Finite Distributed Lag Models

10-2c A Convention about the Time Index

10-3 Finite Sample Properties of OLS under Classical Assumptions

10-3a Unbiasedness of OLS

10-3b The Variances of the OLS Estimators and the Gauss-Markov Theorem

10-3c Inference under the Classical Linear Model Assumptions

10-4 Functional Form, Dummy Variables, and Index Numbers

10-5 Trends and Seasonality

10-5a Characterizing Trending Time Series

10-5b Using Trending Variables in Regression Analysis

10-5c A Detrending Interpretation of Regressions with a Time Trend

10-5d Computing R-Squared When the Dependent Variable Is Trending

10-5e Seasonality

Summary

Key Terms

Problems

Computer Exercises

Chapter 11: Further Issues in Using OLS with Time Series Data

11-1 Stationary and Weakly Dependent Time Series

11-1a Stationary and Nonstationary Time Series

11-1b Weakly Dependent Time Series

11-2 Asymptotic Properties of OLS

11-3 Using Highly Persistent Time Series in Regression Analysis

11-3a Highly Persistent Time Series

11-3b Transformations on Highly Persistent Time Series

11-3c Deciding Whether a Time Series Is I(1)

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

Chapter 12: Serial Correlation and Heteroskedasticity in Time Series Regressions

12-1 Properties of OLS with Serially Correlated Errors

12-1a Unbiasedness and Consistency

12-1b Efficiency and Inference

12-1c Goodness-of-Fit

12-1d Serial Correlation in the Presence of Lagged Dependent Variables

12-2 Serial Correlation–Robust Inference after OLS

12-3 Testing for Serial Correlation

12-3a A t Test for AR(1) Serial Correlation with Strictly Exogenous Regressors

12-3b The Durbin-Watson Test under Classical Assumptions

12-3c Testing for AR(1) Serial Correlation without Strictly Exogenous Regressors

12-3d Testing for Higher-Order Serial Correlation

12-4 Correcting for Serial Correlation with Strictly Exogenous Regressors

12-4a Obtaining the Best Linear Unbiased Estimator in the AR(1) Model

12-4b Feasible GLS Estimation with AR(1) Errors

12-4c Comparing OLS and FGLS

12-4d Correcting for Higher-Order Serial Correlation

12-4e What if the Serial Correlation Model Is Wrong?

12-5 Differencing and Serial Correlation

12-6 Heteroskedasticity in Time Series Regressions

12-6a Heteroskedasticity-Robust Statistics

12-6b Testing for Heteroskedasticity

12-6c Autoregressive Conditional Heteroskedasticity

12-6d Heteroskedasticity and Serial Correlation in Regression Models

Summary

Key Terms

Problems

Computer Exercises

Part 3: Advanced Topics

Chapter 13: Pooling Cross Sections across Time: Simple Panel Data Methods

13-1 Pooling Independent Cross Sections across Time

13-1a The Chow Test for Structural Change across Time

13-2 Policy Analysis with Pooled Cross Sections

13-2a Adding an Additional Control Group

13-2b A General Framework for Policy Analysis with Pooled Cross Sections

13-3 Two-Period Panel Data Analysis

13-3a Organizing Panel Data

13-4 Policy Analysis with Two-Period Panel Data

13-5 Differencing with More Than Two Time Periods

13-5a Potential Pitfalls in First Differencing Panel Data

Summary

Key Terms

Problems

Computer Exercises

Chapter 14: Advanced Panel Data Methods

14-1 Fixed Effects Estimation

14-1a The Dummy Variable Regression

14-1b Fixed Effects or First Differencing?

14-1c Fixed Effects with Unbalanced Panels

14-2 Random Effects Models

14-2a Random Effects or Pooled OLS?

14-2b Random Effects or Fixed Effects?

14-3 The Correlated Random Effects Approach

14-3a Unbalanced Panels

14-4 General Policy Analysis with Panel Data

14-4a Advanced Considerations with Policy Analysis

14-5 Applying Panel Data Methods to Other Data Structures

Summary

Key Terms

Problems

Computer Exercises

Chapter 15: Instrumental Variables Estimation and Two-Stage Least Squares

15-1 Motivation: Omitted Variables in a Simple Regression Model

15-1a Statistical Inference with the IV Estimator

15-1b Properties of IV with a Poor Instrumental Variable

15-1c Computing R-Squared after IV Estimation

15-2 IV Estimation of the Multiple Regression Model

15-3 Two-Stage Least Squares

15-3a A Single Endogenous Explanatory Variable

15-3b Multicollinearity and 2SLS

15-3c Detecting Weak Instruments

15-3d Multiple Endogenous Explanatory Variables

15-3e Testing Multiple Hypotheses after 2SLS Estimation

15-4 IV Solutions to Errors-in-Variables Problems

15-5 Testing for Endogeneity and Testing Overidentifying Restrictions

15-5a Testing for Endogeneity

15-5b Testing Overidentification 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

Chapter 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

16-3a Identification in a Two-Equation System

16-3b Estimation by 2SLS

16-4 Systems with More Than Two Equations

16-4a Identification in Systems with Three or More Equations

16-4b Estimation

16-5 Simultaneous Equations Models with Time Series

16-6 Simultaneous Equations Models with Panel Data

Summary

Key Terms

Problems

Computer Exercises

Chapter 17: Limited Dependent Variable Models and Sample Selection Corrections

17-1 Logit and Probit Models for Binary Response

17-1a Specifying Logit and Probit Models

17-1b Maximum Likelihood Estimation of Logit and Probit Models

17-1c Testing Multiple Hypotheses

17-1d Interpreting the Logit and Probit Estimates

17-2 The Tobit Model for Corner Solution Responses

17-2a Interpreting the Tobit Estimates

17-2b Specification Issues in Tobit Models

17-3 The Poisson Regression Model

17-4 Censored and Truncated Regression Models

17-4a Censored Regression Models

17-4b Truncated Regression Models

17-5 Sample Selection Corrections

17-5a When Is OLS on the Selected Sample Consistent?

17-5b Incidental Truncation

Summary

Key Terms

Problems

Computer Exercises

Chapter 18: Advanced Time Series Topics

18-1 Infinite Distributed Lag Models

18-1a The Geometric (or Koyck) Distributed Lag Model

18-1b Rational Distributed Lag Models

18-2 Testing for Unit Roots

18-3 Spurious Regression

18-4 Cointegration and Error Correction Models

18-4a Cointegration

18-4b Error Correction Models

18-5 Forecasting

18-5a Types of Regression Models Used for Forecasting

18-5b One-Step-Ahead Forecasting

18-5c Comparing One-Step-Ahead Forecasts

18-5d Multiple-Step-Ahead Forecasts

18-5e Forecasting Trending, Seasonal, and Integrated Processes

Summary

Key Terms

Problems

Computer Exercises

Chapter 19: Carrying Out an Empirical Project

19-1 Posing a Question

19-2 Literature Review

19-3 Data Collection

19-3a Deciding on the Appropriate Data Set

19-3b Entering and Storing Your Data

19-3c Inspecting, Cleaning, and Summarizing Your Data

19-4 Econometric Analysis

19-5 Writing an Empirical Paper

19-5a Introduction

19-5b Conceptual (or Theoretical) Framework

19-5c Econometric Models and Estimation Methods

19-5d The Data

19-5e Results

19.5f Conclusions

19-5g Style Hints

Summary

Key Terms

Sample Empirical Projects

List of Journals

Data Sources

Math Refresher A Basic Mathematical Tools

A-1 The Summation Operator and Descriptive Statistics

A-2 Properties of Linear Functions

A-3 Proportions and Percentages

A-4 Some Special Functions and Their Properties

A-4a Quadratic Functions

A-4b The Natural Logarithm

A-4c The Exponential Function

A-5 Differential Calculus

Summary

Key Terms

Problems

Math Refresher B Fundamentals of Probability

B-1 Random Variables and Their Probability Distributions

B-1a Discrete Random Variables

B-1b Continuous Random Variables

B-2 Joint Distributions, Conditional Distributions, and Independence

B-2a Joint Distributions and Independence

B-2b Conditional Distributions

B-3 Features of Probability Distributions

B-3a A Measure of Central Tendency: The Expected Value

B-3b Properties of Expected Values

B-3c Another Measure of Central Tendency: The Median

B-3d Measures of Variability: Variance and Standard Deviation

B-3e Variance

B-3f Standard Deviation

B-3g Standardizing a Random Variable

B-3h Skewness and Kurtosis

B-4 Features of Joint and Conditional Distributions

B-4a Measures of Association: Covariance and Correlation

B-4b Covariance

B-4c Correlation Coefficient

B-4d Variance of Sums of Random Variables

B-4e Conditional Expectation

B-4f Properties of Conditional Expectation

B-4g Conditional Variance

B-5 The Normal and Related Distributions

B-5a The Normal Distribution

B-5b The Standard Normal Distribution

B-5c Additional Properties of the Normal Distribution

B-5d The Chi-Square Distribution

B-5e The t Distribution

B-5f The F Distribution

Summary

Key Terms

Problems

Math Refresher C Fundamentals of Mathematical Statistics

C-1 Populations, Parameters, and Random Sampling

C-1a Sampling

C-2 Finite Sample Properties of Estimators

C-2a Estimators and Estimates

C-2b Unbiasedness

C-2c The Sampling Variance of Estimators

C-2d Efficiency

C-3 Asymptotic or Large Sample Properties of Estimators

C-3a Consistency

C-3b Asymptotic Normality

C-4 General Approaches to Parameter Estimation

C-4a Method of Moments

C-4b Maximum Likelihood

C-4c Least Squares

C-5 Interval Estimation and Confidence Intervals

C-5a The Nature of Interval Estimation

C-5b Confidence Intervals for the Mean from a Normally Distributed Population

C-5c A Simple Rule of Thumb for a 95% Confidence Interval

C-5d Asymptotic Confidence Intervals for Nonnormal Populations

C-6 Hypothesis Testing

C-6a Fundamentals of Hypothesis Testing

C-6b Testing Hypotheses about the Mean in a Normal Population

C-6c Asymptotic Tests for Nonnormal Populations

C-6d Computing and Using p-Values

C-6e The Relationship between Confidence Intervals and Hypothesis Testing

C-6f Practical versus Statistical Significance

C-7 Remarks on Notation

Summary

Key Terms

Problems

Advanced Treatment D Summary of Matrix Algebra

D-1 Basic Definitions

D-2 Matrix Operations

D-2a Matrix Addition

D-2b Scalar Multiplication

D-2c Matrix Multiplication

D-2d Transpose

D-2e Partitioned Matrix Multiplication

D-2f Trace

D-2g Inverse

D-3 Linear Independence and Rank of a Matrix

D-4 Quadratic Forms and Positive Definite Matrices

D-5 Idempotent Matrices

D-6 Differentiation of Linear and Quadratic Forms

D-7 Moments and Distributions of Random Vectors

D-7a Expected Value

D-7b Variance-Covariance Matrix

D-7c Multivariate Normal Distribution

D-7d Chi-Square Distribution

D-7e t Distribution

D-7f F Distribution

Summary

Key Terms

Problems

Advanced Treatment E The Linear Regression Model in Matrix Form

E-1 The Model and Ordinary Least Squares Estimation

E-1a The Frisch-Waugh Theorem

E-2 Finite Sample Properties of OLS

E-3 Statistical Inference

E-4 Some Asymptotic Analysis

E-4a Wald Statistics for Testing Multiple Hypotheses

Summary

Key Terms

Problems

Answers to Going Further Questions

Statistical Tables

References

Glossary

Index

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