Introductory Econometrics: A Modern Approach (7th Edition)

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.


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Jeffrey M. Wooldridge


Cengage Learning; 7th edition




816 pages







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.

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Table of contents

Table of contents :
Brief 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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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)
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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
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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
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
Key Terms
Answers to Going Further Questions
Statistical Tables


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