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Introduction to Econometrics, Brief Edition

by: Stock, James H.; Watson, Mark W.

Introduction to Econometrics, Brief Edition

by: Stock, James H.; Watson, Mark W.

ISBN 13:
9780321432513
ISBN 10:
0321432517
Edition: 1st
Format: Paperback
Copyright: 01/09/2007
Publisher: Pearson

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Summary

Modern econometric theory, motivated by real-world issues Is it more effective to tackle drunk driving by passing tough laws or by increasing the tax on alcohol? Stock and Watson give students the tools to explore policy questions like this using actual data. Each chapter is motivated by a real-world question so students can apply the theory immediately. Contemporary theory and practice reflect the ideas and procedures used by today's professionals, researchers, and policymakers. Common-sense explanations introduce new concepts, facilitating mastery of both basic and technical topics. An opening chapter about statistics and probability includes a review of major ideas students need for the course, such as sampling variability and distribution. Book jacket.

Preface	p. xix
Introduction and Review	p. 1
Economic Questions and Data	p. 3
Economic Questions We Examine	p. 4
Does Reducing Class Size Improve Elementary School Education?	p. 4
What Are the Economic Returns to Education?	p. 5
Quantitative Questions, Quantitative Answers	p. 6
Causal Effects and Idealized Experiments	p. 6
Estimation of Causal Effects	p. 6
Forecasting and Causality	p. 8
Data: Sources and Types	p. 8
Experimental versus Observational Data	p. 8
Cross-Sectional Data	p. 9
Time Series Data	p. 10
Panel Data	p. 11
Review of Probability	p. 15
Random Variables and Probability Distributions	p. 16
Probabilities, the Sample Space, and Random Variables	p. 16
Probability Distribution of a Discrete Random Variable	p. 17
Probability Distribution of a Continuous Random Variable	p. 19
Expected Values, Mean, and Variance	p. 21
The Expected Value of a Random Variable	p. 21
The Standard Deviation and Variance	p. 22
Mean and Variance of a Linear Function of a Random Variable	p. 23
Other Measures of the Shape of a Distribution	p. 24
Two Random Variables	p. 27
Joint and Marginal Distributions	p. 27
Conditional Distributions	p. 28
Independence	p. 32
Covariance and Correlation	p. 32
The Mean and Variance of Sums of Random Variables	p. 33
The Normal, Chi-Squared, Student t, and F Distributions	p. 37
The Normal Distribution	p. 37
The Chi-Squared Distribution	p. 41
The Student t Distribution	p. 42
The F Distribution	p. 42
Random Sampling and the Distribution of the Sample Average	p. 43
Random Sampling	p. 43
The Sampling Distribution of the Sample Average	p. 44
Large-Sample Approximations to Sampling Distributions	p. 46
The Law of Large Numbers and Consistency	p. 47
The Central Limit Theorem	p. 50
Derivation of Results in Key Concept 2.3	p. 61
Review of Statistics	p. 63
Estimation of the Population Mean	p. 64
Estimators and Their Properties	p. 65
Properties of Y	p. 66
The Importance of Random Sampling	p. 68
Hypothesis Tests Concerning the Population Mean	p. 69
Null and Alternative Hypotheses	p. 70
The p-Value	p. 70
Calculating the p-Value When [sigma subscript Y] Is Known	p. 72
The Sample Variance, Sample Standard Deviation, and Standard Error	p. 73
Calculating the p-Value When [sigma subscript Y] Is Unknown	p. 74
The t-Statistic	p. 75
Hypothesis Testing with a Prespecified Significance Level	p. 76
One-Sided Alternatives	p. 78
Confidence Intervals for the Population Mean	p. 79
Comparing Means from Different Populations	p. 81
Hypothesis Tests for the Difference Between Two Means	p. 81
Confidence Intervals for the Difference Between Two Population Means	p. 82
Differences-of-Means Estimation of Causal Effects Using Experimental Data	p. 83
The Causal Effect as a Difference of Conditional Expectations	p. 83
Estimation of the Causal Effect Using Differences of Means	p. 85
Using the t-Statistic When the Sample Size Is Small	p. 86
The t-Statistic and the Student t Distribution	p. 86
Use of the Student t Distribution in Practice	p. 90
Scatterplot, the Sample Covariance, and the Sample Correlation	p. 90
Scatterplots	p. 91
Sample Covariance and Correlation	p. 92
The U.S. Current Population Survey	p. 103
Two Proofs That Y Is the Least Squares Estimator of [mu subscript Y]	p. 104
A Proof That the Sample Variance Is Consistent	p. 105
Fundamentals of Regression Analysis	p. 107
Linear Regression with One Regressor	p. 109
The Linear Regression Model	p. 110
Estimating the Coefficients of the Linear Regression Model	p. 114
The Ordinary Least Squares Estimator	p. 116
OLS Estimates of the Relationship Between Test Scores and the Student-Teacher Ratio	p. 118
Why Use the OLS Estimator?	p. 119
Measures of Fit	p. 121
The R[superscript 2]	p. 121
The Standard Error of the Regression	p. 122
Application to the Test Score Data	p. 123
The Least Squares Assumptions	p. 124
The Conditional Distribution of u[subscript i] Given X[subscript i] Has a Mean of Zero	p. 124
(X[subscript i], Y[subscript i]), i = 1, ..., n Are Independently and Identically Distributed	p. 126
Large Outliers Are Unlikely	p. 127
Use of the Least Squares Assumptions	p. 128
The Sampling Distribution of the OLS Estimators	p. 129
The Sampling Distribution of the OLS Estimators	p. 130
Conclusion	p. 133
The California Test Score Data Set	p. 141
Derivation of the OLS Estimators	p. 141
Sampling Distribution of the OLS Estimator	p. 142
Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals	p. 146
Testing Hypotheses About One of the Regression Coefficients	p. 147
Two-Sided Hypotheses Concerning [Beta subscript 1]	p. 147
One-Sided Hypotheses Concerning [Beta subscript 1]	p. 151
Testing Hypotheses About the Intercept [Beta subscript 0]	p. 153
Confidence Intervals for a Regression Coefficient	p. 153
Regression When X Is a Binary Variable	p. 156
Interpretation of the Regression Coefficients	p. 156
Heteroskedasticity and Homoskedasticity	p. 158
What Are Heteroskedasticity and Homoskedasticity?	p. 158
Mathematical Implications of Homoskedasticity	p. 161
What Does This Mean in Practice?	p. 162
The Theoretical Foundations of Ordinary Least Squares	p. 164
Linear Conditionally Unbiased Estimators and the Gauss-Markov Theorem	p. 165
Regression Estimators Other Than OLS	p. 166
Using the t-Statistic in Regression When the Sample Size is Small	p. 167
The t-Statistic and the Student t Distribution	p. 168
Use of the Student t Distribution in Practice	p. 168
Conclusion	p. 169
Formulas for OLS Standard Errors	p. 178
The Gauss-Markov Conditions and a Proof of the Gauss-Markov Theorem	p. 180
Linear Regression with Multiple Regressors	p. 184
Omitted Variable Bias	p. 184
Definition of Omitted Variable Bias	p. 185
A Formula for Omitted Variable Bias	p. 187
Addressing Omitted Variable Bias by Dividing the Data into Groups	p. 189
The Multiple Regression Model	p. 191
The Population Regression Line	p. 191
The Population Multiple Regression Model	p. 192
The OLS Estimator in Multiple Regression	p. 194
The OLS Estimator	p. 195
Application to Test Scores and the Student-Teacher Ratio	p. 196
Measures of Fit in Multiple Regression	p. 198
The Standard Error of the Regression (SER)	p. 198
The R[superscript 2]	p. 198
The "Adjusted R[superscript 2]"	p. 199
Application to Test Scores	p. 200
The Least Squares Assumptions in Multiple Regression	p. 200
The Conditional Distribution of u[subscript i] Given X[subscript 1i], [subscript 2i], ..., X[subscript ki] Has a Mean of Zero	p. 201
(X[subscript 1i], X[subscript 2i], ..., X[subscript ki], Y[subscript i]) i = 1, ..., n Are i.i.d.	p. 201
Large Outliers Are Unlikely	p. 201
No Perfect Multicollinearity	p. 201
The Distribution of the OLS Estimators in Multiple Regression	p. 203
Multicollinearity	p. 204
Examples of Perfect Multicollinearity	p. 204
Imperfect Multicollinearity	p. 207
Conclusion	p. 208
Derivation of Equation (6.1)	p. 216
Distribution of the OLS Estimators When There Are Two Regressors and Homoskedastic Errors	p. 216
The OLS Estimator With Two Regressors	p. 217
Hypothesis Tests and Confidence Intervals in Multiple Regression	p. 218
Hypothesis Tests and Confidence Intervals for a Single Coefficient	p. 219
Standard Errors for the OLS Estimators	p. 219
Hypothesis Tests for a Single Coefficient	p. 219
Confidence Intervals for a Single Coefficient	p. 221
Application to Test Scores and the Student-Teacher Ratio	p. 221
Tests of Joint Hypotheses	p. 223
Testing Hypotheses on Two or More Coefficients	p. 223
The F-Statistic	p. 225
Application to Test Scores and the Student-Teacher Ratio	p. 227
The Homoskedasticity-Only F-Statistic	p. 228
Testing Single Restrictions Involving Multiple Coefficients	p. 230
Confidence Sets for Multiple Coefficients	p. 232
Model Specification for Multiple Regression	p. 233
Omitted Variable Bias in Multiple Regression	p. 234
Model Specification in Theory and in Practice	p. 234
Interpreting the R[superscript 2] and tine Adjusted R[superscript 2] in Practice	p. 235
Analysis of the Test Score Data Set	p. 237
Conclusion	p. 242
The Bonferroni Test of a Joint Hypotheses	p. 249
Nonlinear Regression Functions	p. 252
A General Strategy for Modeling Nonlinear Regression Functions	p. 254
Test Scores and District Income	p. 254
The Effect on Y of a Change in X in Nonlinear Specifications	p. 258
A General Approach to Modeling Nonlinearities Using Multiple Regression	p. 262
Nonlinear Functions of a Single Independent Variable	p. 262
Polynomials	p. 263
Logarithms	p. 265
Polynomial and Logarithmic Models of Test Scores and District Income	p. 273
Interactions Between Independent Variables	p. 275
Interactions Between Two Binary Variables	p. 275
Interactions Between a Continuous and a Binary Variable	p. 278
Interactions Between Two Continuous Variables	p. 284
Nonlinear Effects on Test Scores of the Student-Teacher Ratio	p. 288
Discussion of Regression Results	p. 289
Summary of Findings	p. 293
Conclusion	p. 294
Regression Functions That Are Nonlinear in the Parameters	p. 305
Assessing Studies Based on Multiple Regression	p. 310
Internal and External Validity	p. 311
Threats to Internal Validity	p. 311
Threats to External Validity	p. 312
Threats to Internal Validity of Multiple Regression Analysis	p. 314
Omitted Variable Bias	p. 314
Misspecification of the Functional Form of the Regression Function	p. 317
Errors-in-Variables	p. 317
Sample Selection	p. 320
Simultaneous Causality	p. 322
Sources of Inconsistency of OLS Standard Errors	p. 323
Internal and External Validity When the Regression Is Used for Forecasting	p. 325
Using Regression Models for Forecasting	p. 325
Assessing the Validity of Regression Models for Forecasting	p. 326
Example: Test Scores and Class Size	p. 327
External Validity	p. 327
Internal Validity	p. 334
Discussion and Implications	p. 335
Conclusion	p. 336
The Massachusetts Elementary School Testing Data	p. 342
Conducting a Regression Study Using Economic Data	p. 343
Choosing a Topic	p. 344
Collecting the Data	p. 345
Finding a Data Set	p. 345
Time Series Data and Panel Data	p. 346
Preparing the Data for Regression Analysis	p. 347
Conducting Your Regression Analysis	p. 347
Writing Up Your Results	p. 348
Appendix	p. 351
References	p. 359
Answers to "Review the Concepts" Questions	p. 361
Glossary	p. 365
Index	p. 371
Table of Contents provided by Ingram. All Rights Reserved.

Amazon no longer offers textbook rentals. We do!

Introduction to Econometrics, Brief Edition

Introduction to Econometrics, Brief Edition

9780321432513

0321432517

Summary

Author Biography

Table of Contents

Supplemental Materials

Write a Review

Amazon no longer offers textbook rentals. We do!

Introduction to Econometrics, Brief Edition

Introduction to Econometrics, Brief Edition

9780321432513

0321432517

Summary

Author Biography

Table of Contents

Supplemental Materials

Write a Review

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