Consequences what does it do to my ols output

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Unformatted text preview: Wednesday 15:00-17:00): Heteroscedasticity 3 May 7 (Friday 14:00-16:00): Measurement Error & Simultaneous Equations All lectures take place in E171 (New Theater) Past Exam Practice Question Introduction Measurement Error Simultaneous Equations Past Exam Practice Question Setup Of Review Lectures Sick Regressions: The Key Questions Common pattern in econometrics: start with basic OLS regression and then analyse what’s wrong Definition: What’s the disease? Consequences: What does it do to my OLS output? Detection: How can I find out if my regression is suffering from the disease? Remedy: What can I do to get rid of the disease? Introduction Measurement Error Simultaneous Equations Past Exam Practice Question Today’s Lecture Difference Between Model A and Model B Up until Chapter 8, we assumed that regressors were nonstochastic. Introduction Measurement Error Simultaneous Equations Past Exam Practice Question Today’s Lecture Difference Between Model A and Model B Up until Chapter 8, we assumed that regressors were nonstochastic. Hence their values were treated like constants. Introduction Measurement Error Simultaneous Equations Past Exam Practice Question Today’s Lecture Difference Between Model A and Model B Up until Chapter 8, we assumed that regressors were nonstochastic. Hence their values were treated like constants. Now introduce stochastic regressors. Introduction Measurement Error Simultaneous Equations Past Exam Practice Question Today’s Lecture Difference Between Model A and Model B Up until Chapter 8, we assumed that regressors were nonstochastic. Hence their values were treated like constants. Now introduce stochastic regressors. Hence their values are treated like realizations of a random variable. Introduction Measurement Error Simultaneous Equations Past Exam Practice Question Today’s Lecture Difference Between Model A and Model B Up until Chapter 8, we assumed that regressors were nonstochastic. Hence their values were treated like constants. Now introduce stochastic regressors. Hence their values are treated like realizations of a random variable. Model A was introduced for analytic simplicity while Model B is more realistic. Introduction Measurement Error Simultaneous Equations Past Exam Practice Question Today’s Lecture Assumptions of Model B Below are the 8 assumptions of Model B: B.1 The model is linear in parameters and correctly specified.(A.1) Introduction Measurement Error Simultaneous Equations Past Exam Practice Question Today’s Lecture Assumptions of Model B Below are the 8 assumptions of Model B: B.1 The model is linear in parameters and correctly specified.(A.1) B.2 Values of regressors are drawn randomly from fixed populations. New! Introduction Measurement Error Simultaneous Equations Past Exam Practice Question Today’s Lecture Assumptions of Model B Below are the 8 assumptions of Model B: B.1 The model is linear in parameters and correctly specified.(A.1) B.2 Values of regressors are drawn rand...
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