Econ 399 Chapter2a

# Econ 399 Chapter2a - Part 1 Cross Sectional Data • Simple...

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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 assumptions-assume people are utility maximizers-assume perfect information-assume we have a can opener • The Simple Regression Model is based on assumptions-more assumptions are required for more analysis-disproving 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 two-variable 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 + + = β β 2.1 The Simple Regression Model • Recall the SIMPLE LINEAR REGRESION MODEL: u is the ERROR TERM or DISTURBANCE variable-u takes into account all factors other than x that affect y-u accounts for all “unobserved” impacts on y (2.1) 1 u x y + + = β β 2.1 The Simple Regression Model • Example of the SIMPLE LINEAR REGRESION MODEL:-taste depends on cooking time-taste is explained by cooking time-taste is a function of cooking time-u accounts for other factors affecting taste (cooking skill, ingredients available, random luck, differing taste buds, etc.) (ie) 1 u e cookingtim taste + + = β β 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 PARAMETER-B is the INTERCEPT PARAMETER or CONSTANT TERM-not always useful in analysis (2.2) u if x y 1 = ∆ ∆ = ∆ β 2.1 The Simple Regression Model-note that this equation implies CONSTANT returns -the first x has the same impact on y as the 100 th x-to avoid this we can include powers or change functional forms (2.1) 1 u x y + + = β β 2.1 The Simple Regression Model-in order to achieve a ceteris paribus analysis of x’s affect on y, we need assumptions of u’s relationship with x-in order to simplify our assumptions, we first assume that the average of u in the population is zero: (2.5) (u) = E-if B o is included in the equation, it can always be modified to make (2.5) true-ie: if E(u)>0, simply increase B 1 2.1 x, u and Dependence2....
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Econ 399 Chapter2a - Part 1 Cross Sectional Data • Simple...

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