heteroskedasticity & serial correlation-sport

heteroskedasticity & serial correlation-sport -...

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Slide # 1 Heteroskedasticity & Serial Correlation * * * * * * * * * * * * * * * * * * * *
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Slide # 2 NOTE Read all slides in the full set of slides for this topic. There is other information in those slides that you must know.
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3 Exam 3 Cumulative exam!! Includes 10 multiple choice questions from Exam 1
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Slide # 4 Research Project Report Due Class #14
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Slide # 5 Research Project: Order of Testing 1 st . Omitted variables and incorrect functional form (F, adj. R 2 , plots) 2 nd : Note: do either B. or C. but not both B. Serial correlation ( time series) C. Heteroskedasticity (cross section) 3 rd . Multicollinearity (corr. matrix, VIF) 4 th . Irrelevant variables (t-statistics)
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Slide # 6 Heteroskedasticity (see notes) * * * * * * * * * * * * * * * * * * * *
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Slide # 7 Heteroskedasticity & Serial Correlation are Sneaky You probably wouldn’t even think that they exist Yet, they can mess up t-statistics so badly that you might reach wrong conclusions about which IVs matter (i.e., affect dependent variable) and which do not
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Slide # 8 II. Introduction A. The word itself 1. Learn to spell "Heteroskedasticity" 2. Will have to spell it on next exam B. Who should learn this? 1. Those using cross-sectional data in their projects (“cross-sectional”??) 2. Those who won't o a) Still need to learn this o b) Can you guess why?
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Slide # 9 Y X * * * * * * * * * * * * * * * * * * * Want dispersion of DV around regression line Want dispersion of DV around regression line ( ( σ σ 2 ) roughly constant across sample ) roughly constant across sample NO HETEROSKEDASTICITY Regression line
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Slide # 10 Y X * * * * * * * * * * * * * * * * * * * average relationship between DV & IV average relationship between DV & IV same throughout sample: same throughout sample: β β constant constant NO HETEROSKEDASTICITY Regression line
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Slide # 11 Y X * * * * * * * * * * * * * * * * * * * Common case: dispersion of DV around Common case: dispersion of DV around regression line ( regression line ( σ σ 2 ) not constant ) not constant * * * * * * * HETEROSKEDASTICITY Regression line
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Slide # 12 average relationship between DV & IV average relationship between DV & IV NOT NOT same throughout sample: same throughout sample: β β NOT constant NOT constant Y X * * * * * * * * * * * * * * * * * * * * * * * * * * HETEROSKEDASTICITY Regression line
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Slide # 13 When can it occur? 1. Example 2. MLB payrolls and market size o smaller market teams Less variability in payroll sizes Lower revenues available for payrolls o Larger market teams More variability in payroll sizes Greater revenues available for payrolls Some teams spend more on players Other teams spend relatively less on players
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Slide # 14 When can it occur? (cont.) MLB payrolls and market size LARGE markets (populations > 4,000,000)** o Standard deviation = $27,940,000 o Range = $97,470,000 Min = $40,960,000 Max = $138,420,000 SMALL markets (populations < 4,000,000) o Standard deviation = $20,850,000 o Range = $73,620,000 Min = $35,880,000 Max = $109,500,000 ** all dollar amounts rounded
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Slide # 15 Payroll Market Size * * * * * * * * * * * * * * * * * * * β β NOT constant NOT constant Small market teams: Small market teams: σ σ 2 small small Large market teams: Large market teams: σ σ 2 large large * * * * HETEROSKEDASTICITY Regression line * * * * *
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Slide # 16 When can it occur? (cont.) o NOTE: same principle applies for many units with wide range of sizes in same sample
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