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Unformatted text preview: Part 1 Cross Sectional Data Simple Linear Regression Model Chapter 2 Multiple Regression Analysis Chapters 3 and 4 Advanced Regression Topics Chapter 6 Dummy Variables Chapter 7 Note: Appendices A, B, and C are additional review if needed. 1. The Simple Regression Model 2.1 Definition of the Simple Regression Model 2.2 Deriving the Ordinary Least Squares Estimates 2.3 Properties of OLS on Any Sample of Data 2.4 Units of Measurement and Functional Form 2.5 Expected Values and Variances of the OLS Estimators 2.6 Regression through the Origin 2.1 The Simple Regression Model Economics is built upon assumptionsassume people are utility maximizersassume perfect informationassume we have a can opener The Simple Regression Model is based on assumptionsmore assumptions are required for more analysisdisproving assumptions leads to more complicated models 2.1 The Simple Regression Model Recall the SIMPLE LINEAR REGRESION MODEL:relates two variables (x and y)also called the twovariable linear regression model or bivariate linear regression model y is the DEPENDENT or EXPLAINED variable x is the INDEPENDENT or EXPLANATORY variable y is a function of x (2.1) 1 u x y + + = 2.1 The Simple Regression Model Recall the SIMPLE LINEAR REGRESION MODEL: u is the ERROR TERM or DISTURBANCE variableu takes into account all factors other than x that affect yu accounts for all unobserved impacts on y (2.1) 1 u x y + + = 2.1 The Simple Regression Model Example of the SIMPLE LINEAR REGRESION MODEL:taste depends on cooking timetaste is explained by cooking timetaste is a function of cooking timeu accounts for other factors affecting taste (cooking skill, ingredients available, random luck, differing taste buds, etc.) (ie) 1 u e cookingtim taste + + = 2.1 The Simple Regression Model The SRM shows how y changes:for example, if B 1 =3, a 2 increase in x would cause a 6 unit change in y (2 x 3 = 6)B 1 is the SLOPE PARAMETERB is the INTERCEPT PARAMETER or CONSTANT TERMnot always useful in analysis (2.2) u if x y 1 = = 2.1 The Simple Regression Modelnote that this equation implies CONSTANT returns the first x has the same impact on y as the 100 th xto avoid this we can include powers or change functional forms (2.1) 1 u x y + + = 2.1 The Simple Regression Modelin order to achieve a ceteris paribus analysis of xs affect on y, we need assumptions of us relationship with xin order to simplify our assumptions, we first assume that the average of u in the population is zero: (2.5) (u) = Eif B o is included in the equation, it can always be modified to make (2.5) trueie: if E(u)>0, simply increase B 1 2.1 x, u and Dependence2....
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 Spring '09
 Priemaza
 Econometrics

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