《計量經濟學》ch-03-wooldridg.ppt

? 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Chapter 3 Multiple Regression Analysis: Estimation Wooldridge: Introductory Econometrics: A Modern Approach, 5e Instructed by professor Yuan, Huiping ? 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. CHAPTER 3 Multiple Regression Analysis: Estimation 3.2 Mechanics and Interpretation of OLS 3.3 The Expected Value of the OLS Estimators 3.4 The Variance of the OLS Estimators 3.5 Efficiency of OLS: The Gauss-Markov Theorem 3.1 Motivation for Multiple Regression 3.6 Some Comments on the Language of Multiple Regression Analysis Assignments: Promblems 7, 9, 10, 11, 13, Computer rcises C1, C3, C5, C6, C8 The End ? 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Definition of the multiple linear regression model Dependent variable, explained variable, response variable,… Independent variables, explanatory variables, regressors,… Error term, disturbance, unobservables,… InterceptSlope parameters ?Explains variable in terms of variables “ 3.1 Motivation for Multiple Regression (1/5) CHAPTER 3 Multiple Regression Analysis: Estimation ChapterEnd ? 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Motivation for multiple regression Incorporate more explanatory factors into the model Explicitly hold fixed other factors that otherwise would be in Allow for more flexible functional s Example: Wage equation Hourly wageYears of educationLabor market experience All other factors… Now measures effect of education explicitly holding experience fixed CHAPTER 3 Multiple Regression Analysis: Estimation 3.1 Motivation for Multiple Regression (2/5) ChapterEnd ? 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Example: Average test scores and per student spending Per student spending is likely to be correlated with average family income at a given high school because of school financing Omitting average family income in regression would lead to biased estimate of the effect of spending on average test scores In a simple regression model, effect of per student spending would partly include the effect of family income on test scores Average standardized test score of school Other factors Per student spending at this school Average family income of students at this school CHAPTER 3 Multiple Regression Analysis: Estimation 3.1 Motivation for Multiple Regression (3/5) ChapterEnd ? 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible webs