Introductory econometrics : a modern approach / Jeffrey M. Wooldridge.

Author
Wooldridge, Jeffrey M., 1960- [Browse]
Format
Book
Language
English
Εdition
5th edition.
Published/​Created
  • Mason, OH : South-Western Cengage Learning, [2013]
  • ©2013
Description
xxv, 881 pages : illustrations ; 24 cm

Availability

Copies in the Library

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Firestone Library - Stacks HB139 .W665 2013 Browse related items Request
    Forrestal Annex - ReserveHB139 .W665 2013 Browse related items Request
      Forrestal Annex - ReserveHB139 .W665 2013 Browse related items Request
        Stokes Library - Wallace Hall (SPIA) HB139 .W665 2013 Browse related items Request
          Stokes Library - Wallace Hall (SPIA) HB139 .W665 2013 Browse related items Request
            Stokes Library - Wallace Hall (SPIA) HB139 .W665 2013 Browse related items Request

              Details

              Subject(s)
              Summary note
              Wooldridge uses a systematic approach motivated by the major problems facing applied researchers. This text provides important understanding for empirical work in many social sciences, as well as for carrying out research projects.
              Bibliographic references
              Includes bibliographical references (p. 838-843) and index.
              Contents
              • Preface (starting p. xv)
              • About the Author (starting p. xxv)
              • ch. 1 The Nature of Economic and Economic Data (starting p. 1)
              • 1.1. What Is Econometrics (starting p. 1)
              • 1.2. Steps in Empirical Economic Analysis (starting p. 2)
              • 1.3. The Structure of Economic Data (starting p. 5)
              • Cross-Sectional Data (starting p. 5)
              • Time Series Data (starting p. 8)
              • Pooled Cross Sections (starting p. 9)
              • Panel or Longitudinal Data (starting p. 10)
              • A Comment on Data Structures (starting p. 11)
              • 1.4. Causality and the Notion of Ceteris Paribus in Econometric Analysis (starting p. 12)
              • Summary (starting p. 16)
              • Key Terms (starting p. 17)
              • Problems (starting p. 17)
              • Computer Exercises (starting p. 17)
              • Part 1 Regression Analysis with Cross-Sectional Data (starting p. 21)
              • ch. 2 The Simple Regression Model (starting p. 22)
              • 2.1. Definition of the Simple Regression Model (starting p. 22)
              • 2.2. Deriving the Ordinary Least Squares Estimates (starting p. 27)
              • A Note of Terminology (starting p. 34)
              • 2.3. Properties of OLS on Any Sample of Data (starting p. 35)
              • Fitted Values and Residuals (starting p. 35)
              • Algebraic Properties of OLS Statistics (starting p. 36)
              • Goodness-of-Fit (starting p. 38)
              • 2.4. Units of Measurement and Functional Form (starting p. 39)
              • The Effects of Changing Units of Measurement on OLS Statistics (starting p. 40)
              • Incorporating Nonlinearities in Simple Regression (starting p. 41)
              • The Meaning of "Linear" Regression (starting p. 44)
              • 2.5. Expected Values and Variances of the OLS Estimators (starting p. 45)
              • Unbiasedness of OLS (starting p. 45)
              • Variances of the OLS Estimators (starting p. 50)
              • Estimating the Error Variance (starting p. 54)
              • 2.6. Regression through the Origin and Regression on a Constant (starting p. 57)
              • Summary (starting p. 58)
              • Key Terms (starting p. 59)
              • Problems (starting p. 60)
              • Computer Exercises (starting p. 63)
              • Appendix 2A (starting p. 66)
              • ch. 3 Multiple Regression Analysis: Estimation (starting p. 68)
              • 3.1. Motivation for Multiple Regression (starting p. 69)
              • The Model with Two Independent Variables (starting p. 69)
              • The Model with k Independent Variables (starting p. 71)
              • 3.2. Mechanics and Interpretation of Ordinary Least Squares (starting p. 72)
              • Obtaining the OLS Estimates (starting p. 72)
              • Interpreting the OLS Regression Equation (starting p. 74)
              • On the Meaning of "Holding Other Factors Fixed" in Multiple Regression (starting p. 76)
              • Changing More Than One Independent Variable Simultaneously (starting p. 77)
              • OLS Fitted Values and Residuals (starting p. 77)
              • A "Portioning Out" Interpretation of Multiple Regression (starting p. 78)
              • Comparison of Simple and Multiple Regression Estimates (starting p. 78)
              • Goodness-of-Fit (starting p. 80)
              • Regression through the Origin (starting p. 81)
              • 3.3. The Expected Value of the OLS Estimators (starting p. 83)
              • Including Irrelevant Variables in a Regression Model (starting p. 88)
              • Omitted Variable Bias: The Simple Case (starting p. 88)
              • Omitted Variable Bias: More General Cases (starting p. 91)
              • 3.4. The Variance of the OLS Estimators (starting p. 93)
              • The Components of the OLS Variances: Multicollinearity (starting p. 94)
              • Variances in Misspecified Models (starting p. 98)
              • Estimating [sigma]2: Standard Errors of the OLS Estimators (starting p. 99)
              • 3.5. Efficiency of OLS: The Gauss-Markov Theorem (starting p. 101)
              • 3.6. Some Comments on the Language of Multiple Regression Analysis (starting p. 103)
              • Summary (starting p. 104)
              • Key Terms (starting p. 105)
              • Problems (starting p. 106)
              • Computer Exercises (starting p. 110)
              • Appendix 3A (starting p. 113)
              • ch. 4 Multiple Regression Analysis: Inference (starting p. 118)
              • 4.1. Sampling Distributions of the OLS Estimators (starting p. 118)
              • 4.2. Testing Hypotheses about a Single Population Parameter: The t Test (starting p. 121)
              • Testing against One-Sided Alternatives (starting p. 123)
              • Two-Sided Alternatives (starting p. 128)
              • Testing Other Hyotheses about pi (starting p. 130)
              • Computing p-Values for t Tests (starting p. 133)
              • A Reminder on the Language of Classical Hypothesis Testing (starting p. 135)
              • Economic, or Practical, versus Statistical Significance (starting p. 135)
              • 4.3. Confidence Intervals (starting p. 138)
              • 4.4. Testing Hypotheses about a Single Linear Combination of the Parameters (starting p. 140)
              • 4.5. Testing Multiple Linear Restrictions: The F Test (starting p. 143)
              • Testing Exclusion Restrictions (starting p. 143)
              • Relationship between F and t Statistics (starting p. 149)
              • The R-Squared Form of the F Statistic (starting p. 150)
              • Computing p-Values for F Tests (starting p. 151)
              • The F Statistic for Overall Significance of a Regression (starting p. 152)
              • Testing General Linear Restrictions (starting p. 153)
              • 4.6. Reporting Regression Results (starting p. 154)
              • Summary (starting p. 157)
              • Key Terms (starting p. 159)
              • Problems (starting p. 159)
              • Computer Exercises (starting p. 164)
              • ch. 5 Multiple Regression Analysis: OLS Asymptotics (starting p. 168)
              • 5.1. Consistency (starting p. 169)
              • Deriving the Inconsistency in OLS (starting p. 172)
              • 5.2. Asymptotic Normality and Large Sample Inference (starting p. 173)
              • Other Large Sample Tests: The Lagrange Multiplier Statistic (starting p. 178)
              • 5.3. Asymptotic Efficiency of OLS (starting p. 181)
              • Summary (starting p. 182)
              • Key Terms (starting p. 183)
              • Problems (starting p. 183)
              • Computer Exercises (starting p. 183)
              • Appendix 5A (starting p. 185)
              • ch. 6 Multiple Regression Analysis: Further Issues (starting p. 186)
              • 6.1. Effects of Data Scaling on OLS Statistics (starting p. 186)
              • Beta Coefficients (starting p. 189)
              • 6.2. More on Functional Form (starting p. 191)
              • More on Using Logarithmic Functional Forms (starting p. 191)
              • Models with Quadratics (starting p. 194)
              • Models with Interaction Terms (starting p. 198)
              • 6.3. More on Goodness-of-Fit and Selection of Regressors (starting p. 200)
              • Adjusted R-Squared (starting p. 202)
              • Using Adjusted R-Squared to Choose between Nonnested Models (starting p. 203)
              • Controlling for Too Many Factors in Regression Analysis (starting p. 205)
              • Adding Regressors to Reduce the Error Variance (starting p. 206)
              • 6.4. Prediction and Residual Analysis (starting p. 207)
              • Confidence Intervals for Predictions (starting p. 207)
              • Residual Analysis (starting p. 211)
              • Predicting y When log(y) Is the Dependent Variable (starting p. 212)
              • Summary (starting p. 216)
              • Key Terms (starting p. 217)
              • Problems (starting p. 218)
              • Computer Exercises (starting p. 220)
              • Appendix 6A (starting p. 225)
              • ch. 7 Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables (starting p. 227)
              • 7.1. Describing Qualitative Information (starting p. 227)
              • 7.2. A Single Dummy Independent Variable (starting p. 228)
              • Interpreting Coefficients on Dummy Explanatory Variables When the Dependent Variable Is log(y) (starting p. 233)
              • 7.3. Using Dummy Variables for Multiple Categories (starting p. 235)
              • Incorporating Ordinal Information by Using Dummy Variables (starting p. 237)
              • 7.4. Interactions Involving Dummy Variables (starting p. 240)
              • Interactions among Dummy Variables (starting p. 240)
              • Allowing for Different Slopes (starting p. 241)
              • Testing for Differences in Regression Functions across Groups (starting p. 245)
              • 7.5. A Binary Dependent Variable: The Linear Probability Model (starting p. 248)
              • 7.6. More on Policy Analysis and Program Evaluation (starting p.
              • Ch. 1. The nature of econometrics and economic data
              • pt. 1. Regression analysis with cross-sectional data
              • Ch. 2. The simple regression model
              • Ch. 3. Multiple regression analysis: estimation
              • Ch. 4. Multiple regression analysis: inference
              • Ch. 5. Multiple regression analysis: OLS asymptotics
              • Ch. 6. Multiple regression analysis: further issues
              • Ch. 7. Multiple regression analysis with qualitative information: binary (or dummy) variables
              • Ch. 8. Hetroskedasticity
              • Ch. 9. More on specification and data issues
              • pt. 2. Regression analysis with time series data
              • Ch. 10. Basic regression analysis with time series data
              • Ch. 11. Further issues in using OLS with time series data
              • Ch. 12. Serial correlation and heteroskedasticity in time series regressions
              • Ch. 13. Pooling cross sections across time: simple panel data methods
              • Ch. 14. Advanced panel data methods
              • Ch. 15. Instrumental variables estimation and two stage least squares
              • Ch. 16. Simultaneous equations models
              • Ch. 17. Limited dependent variable models and sample selection corrections
              • Ch. 18. Advanced time series topics
              • Ch. 19. Carrying out an empirical project
              • Appendices.
              ISBN
              • 1111531048 ((hbk.))
              • 9781111531041 ((hbk.))
              • 9781111534394
              • 111153439X
              LCCN
              2012945120
              OCLC
              827938223
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