10959nam a22004813i 4500 EBC6351340 MiAaPQ 20260706084516.0 m o d | cr cnu|||||||| 260706s2019 xx o ||||0 eng d 9781337671330 (electronic bk.) 9781337558860 (MiAaPQ)EBC6351340 (Au-PeEL)EBL6351340 (OCoLC)1202453529 MiAaPQ eng rda pn MiAaPQ MiAaPQ HB139 .W665 2020 330.015195 Wooldridge, Jeffrey M. Introductory Econometrics : a Modern Approach. 7th ed. Mason, OH : Cengage, 2019. �2020. 1 online resource (849 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Intro -- Brief Contents -- 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 -- 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 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 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 -- 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 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 -- 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 -- 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 -- 6.4 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) -- 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 -- 7-7 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 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 -- 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. 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