Introductory Econometrics

Name: Introductory Econometrics
Price: 4990 ISK
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Author: Jeffrey M. Wooldridge
ISBN: 9798214493534

Höfundur: Jeffrey M. Wooldridge (Útgáfa: 8)

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Útgefandi: Cengage Learning EMEA

ISBN: 9798214493534

Print ISBN: 9798214050317

Format: Page Fidelity (PDF)

Útgáfa: 8

Höfundar: Jeffrey M. Wooldridge

Tungumál: English

Útgefið: 04/2025

Prent takmörkun á líftíma: 100

Prent takmörkun: 2

Afritunar takmörkun: 2

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Introductory Econometrics: A Modern Approach
Brief Contents
Contents
Preface
About the Author
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 (or Repeated) 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 (or Potential) 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 : 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
3-8: Including “Bad Controls” in Regression 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
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
4-2g: Should p-Values Be Abandoned?
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
5-4: Testing Normality of the Error Term
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
6-4a: 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
7-6b: Regression Adjustment under Randomized Assignment
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 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: Adding Another Control Period
13-2c: 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 and Two-Way Fixed Effects
14-1b: Fixed Effects or First Differencing?
14-1c: Fixed Effects with Unbalanced Panels
14-2: Random Effects Estimation
14-2a: Random Effects or Pooled OLS?
14-2b: Random Effects or Fixed Effects?
14-2c: Computing Robust Standard Errors
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
15-2a: The IV Estimator as the Ratio of Two OLS Estimators
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
17-1e: Computing APEs in More Complicated Settings
17-2: Fractional Response Models
17-3: An Exponential Mean Model and Poisson Regression
17-4: The Tobit Model for Corner Solution Responses
17-4a: Interpreting the Tobit Estimates
17-4b: Specification Issues in Tobit Models
17-5: Censored and Truncated Regression Models
17-5a: Censored Regression Models
17-5b: Truncated Regression Models
17-6: Sample Selection Corrections
17-6a: When Is OLS on the Selected Sample Consistent?
17-6b: 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
18-6: Event Studies with Control Groups
Summary
Key Terms
Problems
Computer Exercises
Chapter 19: Advanced Methods for Causal Inference
19-1: Potential Outcomes@ Parameters@ and Assumptions
19-2: Regression Adjustment
19-2a: Linear Regression Adjustment
19-2b: Regression Adjustment with Nonlinear Models
19-2c: Regression Adjustment with Insufficient Overlap
19-2d: Regression Adjustment under Random Assignment
19-3: Inverse Probability Weighting
19-3a Using the Propensity Score to Study Overlap
19-4: Combining IPW and RA
19-5: Instrumental Variables and the Local Average Treatment Effect
19-5a: Treatment Potential Outcomes and LATE
19-5b: Estimating LATE with Covariates
19-5c: Randomized Instrument with Covariates
19-6: Regression Discontinuity Designs
19-6a: Introduction and Motivation
19-6b: The Sharp RD Design
19-6c: The Fuzzy RD Design
19-6d: Relaxing Unconfoundedness In The Frd Design
19-6e: Additional Analyses
19-7: Control Function Estimation of Treatment Effects
19-7a: Treatment Effects in an Endogenous Switching Model
19-7b: 2SLS Estimation of the Endogenous Switching Model
19-7c: Control Function Estimation
19-7d: Estimating the ATT
19-7e: A Binary Instrument@ Revisited
Key Terms
Problems
Computer Exercises
Chapter 20: Carrying Out an Empirical Project
20-1: Posing a Question
20-2: Literature Review
20-3: Data Collection
20-3a: Deciding on the Appropriate Data Set
20-3b: Entering and Storing Your Data
20-3c: Inspecting@ Cleaning@ and Summarizing Your Data
20-4: Econometric Analysis
20-5: Writing an Empirical Paper
20-5a: Introduction
20-5b: Conceptual (or Theoretical) Framework
20-5c: Econometric Models and Estimation Methods
20-5d: The Data
20-5e: Results
20-5f: Conclusions
20-5g: 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: Exact Statistical 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