Introduction to Econometrics

ISBN-10: 0321432517
ISBN-13: 9780321432513
Edition: 2008 (Brief Edition)
List price: $240.80 Buy it from $78.00
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Description: KEY MESSAGE:In keeping with their successful introductory econometrics text, Stock and Watson motivate each methodological topic with a real-world policy application that uses data, so that readers apply the theory immediately.Introduction to  More...

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Book details

List price: $240.80
Copyright year: 2008
Publisher: Addison Wesley
Publication date: 1/9/2007
Binding: Paperback
Pages: 544
Size: 7.50" wide x 9.25" long x 0.75" tall
Weight: 2.046

KEY MESSAGE:In keeping with their successful introductory econometrics text, Stock and Watson motivate each methodological topic with a real-world policy application that uses data, so that readers apply the theory immediately.Introduction to Econometrics, Brief,is a streamlined version of their text, including the fundamental topics, an early review of statistics and probability, the core material of regression with cross-sectional data, and a capstone chapter on conducting empirical analysis. Introduction and Review:Economic Questions and Data; Review of Probability; Review of Statistics.Fundamentals of Regression Analysis:Linear Regression with One Regressor; Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals in the Single-Regressor Model; Linear Regression with Multiple Regressors; Hypothesis Tests and Confidence Intervals in the Multiple Regressor Model; Nonlinear Regression Functions; Assessing Studies Based on Multiple Regression; Conducting a Regression Study Using Economic Data. MARKET: For all readers interested in econometrics. nbsp; nbsp;

Preface
Introduction and Review
Economic Questions and Data
Economic Questions We Examine
Does Reducing Class Size Improve Elementary School Education?
What Are the Economic Returns to Education?
Quantitative Questions, Quantitative Answers
Causal Effects and Idealized Experiments
Estimation of Causal Effects
Forecasting and Causality
Data: Sources and Types
Experimental versus Observational Data
Cross-Sectional Data
Time Series Data
Panel Data
Review of Probability
Random Variables and Probability Distributions
Probabilities, the Sample Space, and Random Variables
Probability Distribution of a Discrete Random Variable
Probability Distribution of a Continuous Random Variable
Expected Values, Mean, and Variance
The Expected Value of a Random Variable
The Standard Deviation and Variance
Mean and Variance of a Linear Function of a Random Variable
Other Measures of the Shape of a Distribution
Two Random Variables
Joint and Marginal Distributions
Conditional Distributions
Independence
Covariance and Correlation
The Mean and Variance of Sums of Random Variables
The Normal, Chi-Squared, Student t, and F Distributions
The Normal Distribution
The Chi-Squared Distribution
The Student t Distribution
The F Distribution
Random Sampling and the Distribution of the Sample Average
Random Sampling
The Sampling Distribution of the Sample Average
Large-Sample Approximations to Sampling Distributions
The Law of Large Numbers and Consistency
The Central Limit Theorem
Derivation of Results in Key Concept 2.3
Review of Statistics
Estimation of the Population Mean
Estimators and Their Properties
Properties of Y
The Importance of Random Sampling
Hypothesis Tests Concerning the Population Mean
Null and Alternative Hypotheses
The p-Value
Calculating the p-Value When [sigma subscript Y] Is Known
The Sample Variance, Sample Standard Deviation, and Standard Error
Calculating the p-Value When [sigma subscript Y] Is Unknown
The t-Statistic
Hypothesis Testing with a Prespecified Significance Level
One-Sided Alternatives
Confidence Intervals for the Population Mean
Comparing Means from Different Populations
Hypothesis Tests for the Difference Between Two Means
Confidence Intervals for the Difference Between Two Population Means
Differences-of-Means Estimation of Causal Effects Using Experimental Data
The Causal Effect as a Difference of Conditional Expectations
Estimation of the Causal Effect Using Differences of Means
Using the t-Statistic When the Sample Size Is Small
The t-Statistic and the Student t Distribution
Use of the Student t Distribution in Practice
Scatterplot, the Sample Covariance, and the Sample Correlation
Scatterplots
Sample Covariance and Correlation
The U.S. Current Population Survey
Two Proofs That Y Is the Least Squares Estimator of [mu subscript Y]
A Proof That the Sample Variance Is Consistent
Fundamentals of Regression Analysis
Linear Regression with One Regressor
The Linear Regression Model
Estimating the Coefficients of the Linear Regression Model
The Ordinary Least Squares Estimator
OLS Estimates of the Relationship Between Test Scores and the Student-Teacher Ratio
Why Use the OLS Estimator?
Measures of Fit
The R[superscript 2]
The Standard Error of the Regression
Application to the Test Score Data
The Least Squares Assumptions
The Conditional Distribution of u[subscript i] Given X[subscript i] Has a Mean of Zero
(X[subscript i], Y[subscript i]), i = 1, ..., n Are Independently and Identically Distributed
Large Outliers Are Unlikely
Use of the Least Squares Assumptions
The Sampling Distribution of the OLS Estimators
The Sampling Distribution of the OLS Estimators
Conclusion
The California Test Score Data Set
Derivation of the OLS Estimators
Sampling Distribution of the OLS Estimator
Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
Testing Hypotheses About One of the Regression Coefficients
Two-Sided Hypotheses Concerning [Beta subscript 1]
One-Sided Hypotheses Concerning [Beta subscript 1]
Testing Hypotheses About the Intercept [Beta subscript 0]
Confidence Intervals for a Regression Coefficient
Regression When X Is a Binary Variable
Interpretation of the Regression Coefficients
Heteroskedasticity and Homoskedasticity
What Are Heteroskedasticity and Homoskedasticity?
Mathematical Implications of Homoskedasticity
What Does This Mean in Practice?
The Theoretical Foundations of Ordinary Least Squares
Linear Conditionally Unbiased Estimators and the Gauss-Markov Theorem
Regression Estimators Other Than OLS
Using the t-Statistic in Regression When the Sample Size is Small
The t-Statistic and the Student t Distribution
Use of the Student t Distribution in Practice
Conclusion
Formulas for OLS Standard Errors
The Gauss-Markov Conditions and a Proof of the Gauss-Markov Theorem
Linear Regression with Multiple Regressors
Omitted Variable Bias
Definition of Omitted Variable Bias
A Formula for Omitted Variable Bias
Addressing Omitted Variable Bias by Dividing the Data into Groups
The Multiple Regression Model
The Population Regression Line
The Population Multiple Regression Model
The OLS Estimator in Multiple Regression
The OLS Estimator
Application to Test Scores and the Student-Teacher Ratio
Measures of Fit in Multiple Regression
The Standard Error of the Regression (SER)
The R[superscript 2]
The "Adjusted R[superscript 2]"
Application to Test Scores
The Least Squares Assumptions in Multiple Regression
The Conditional Distribution of u[subscript i] Given X[subscript 1i], [subscript 2i], ..., X[subscript ki] Has a Mean of Zero
(X[subscript 1i], X[subscript 2i], ..., X[subscript ki], Y[subscript i]) i = 1, ..., n Are i.i.d.
Large Outliers Are Unlikely
No Perfect Multicollinearity
The Distribution of the OLS Estimators in Multiple Regression
Multicollinearity
Examples of Perfect Multicollinearity
Imperfect Multicollinearity
Conclusion
Derivation of Equation (6.1)
Distribution of the OLS Estimators When There Are Two Regressors and Homoskedastic Errors
The OLS Estimator With Two Regressors
Hypothesis Tests and Confidence Intervals in Multiple Regression
Hypothesis Tests and Confidence Intervals for a Single Coefficient
Standard Errors for the OLS Estimators
Hypothesis Tests for a Single Coefficient
Confidence Intervals for a Single Coefficient
Application to Test Scores and the Student-Teacher Ratio
Tests of Joint Hypotheses
Testing Hypotheses on Two or More Coefficients
The F-Statistic
Application to Test Scores and the Student-Teacher Ratio
The Homoskedasticity-Only F-Statistic
Testing Single Restrictions Involving Multiple Coefficients
Confidence Sets for Multiple Coefficients
Model Specification for Multiple Regression
Omitted Variable Bias in Multiple Regression
Model Specification in Theory and in Practice
Interpreting the R[superscript 2] and tine Adjusted R[superscript 2] in Practice
Analysis of the Test Score Data Set
Conclusion
The Bonferroni Test of a Joint Hypotheses
Nonlinear Regression Functions
A General Strategy for Modeling Nonlinear Regression Functions
Test Scores and District Income
The Effect on Y of a Change in X in Nonlinear Specifications
A General Approach to Modeling Nonlinearities Using Multiple Regression
Nonlinear Functions of a Single Independent Variable
Polynomials
Logarithms
Polynomial and Logarithmic Models of Test Scores and District Income
Interactions Between Independent Variables
Interactions Between Two Binary Variables
Interactions Between a Continuous and a Binary Variable
Interactions Between Two Continuous Variables
Nonlinear Effects on Test Scores of the Student-Teacher Ratio
Discussion of Regression Results
Summary of Findings
Conclusion
Regression Functions That Are Nonlinear in the Parameters
Assessing Studies Based on Multiple Regression
Internal and External Validity
Threats to Internal Validity
Threats to External Validity
Threats to Internal Validity of Multiple Regression Analysis
Omitted Variable Bias
Misspecification of the Functional Form of the Regression Function
Errors-in-Variables
Sample Selection
Simultaneous Causality
Sources of Inconsistency of OLS Standard Errors
Internal and External Validity When the Regression Is Used for Forecasting
Using Regression Models for Forecasting
Assessing the Validity of Regression Models for Forecasting
Example: Test Scores and Class Size
External Validity
Internal Validity
Discussion and Implications
Conclusion
The Massachusetts Elementary School Testing Data
Conducting a Regression Study Using Economic Data
Choosing a Topic
Collecting the Data
Finding a Data Set
Time Series Data and Panel Data
Preparing the Data for Regression Analysis
Conducting Your Regression Analysis
Writing Up Your Results
Appendix
References
Answers to "Review the Concepts" Questions
Glossary
Index

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