- We Use The Data In Smoke To Estimate A Demand Function For Daily Cigarette Consumption 1 (47.66 KiB) Viewed 22 times
we use the data in smoke to estimate a demand function for daily cigarette consumption.
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we use the data in smoke to estimate a demand function for daily cigarette consumption.
we use the data in smoke to estimate a demand function for daily cigarette consumption.
6- We use the data in SMOKE to estimate a demand function for daily cigarette consumption. The equation estimated by ordinary least squares, with the usual OLS standard errors in parentheses, is cigs = -3.64 +880 log(income)- 751 log(cigpric) (24.08) (728) (5.773) -501 educ+.771 age-0090 age² - 2.83 restaurn (.167) (.160) (.0017) (1.11) n = 807, R² = .0526, where cigs number of cigarettes smoked per day. Income annual income. cigpric=the per-pack price of cigarettes (in cents). educ=years of schooling. age=age measured in years. restaurn a binary indicator equals unity if the person resides in a state with restaurant smoking restrictions. We compute its determination coefficient by computing the auxiliary regression, obtaining a value of R=0.040. a- Test the first equation for heteroscedasticity at 5% significance level by using Braush- Pagan test. b- Using the feasible GLS procedure based on the equation, the weighted least squares estimates are calculated. Interpret the equation and emphasize the significant differences between these two models. cigs 5.64+ 1.30 log(income) - 2.94 log(cigpric) (17.80) (44) (4.46) -463 educ+ 482 age-0056 age²3.46 restaurn (.097) (.0009) (.80) (.120) n=807, R² = .1134.
6- We use the data in SMOKE to estimate a demand function for daily cigarette consumption. The equation estimated by ordinary least squares, with the usual OLS standard errors in parentheses, is cigs = -3.64 +880 log(income)- 751 log(cigpric) (24.08) (728) (5.773) -501 educ+.771 age-0090 age² - 2.83 restaurn (.167) (.160) (.0017) (1.11) n = 807, R² = .0526, where cigs number of cigarettes smoked per day. Income annual income. cigpric=the per-pack price of cigarettes (in cents). educ=years of schooling. age=age measured in years. restaurn a binary indicator equals unity if the person resides in a state with restaurant smoking restrictions. We compute its determination coefficient by computing the auxiliary regression, obtaining a value of R=0.040. a- Test the first equation for heteroscedasticity at 5% significance level by using Braush- Pagan test. b- Using the feasible GLS procedure based on the equation, the weighted least squares estimates are calculated. Interpret the equation and emphasize the significant differences between these two models. cigs 5.64+ 1.30 log(income) - 2.94 log(cigpric) (17.80) (44) (4.46) -463 educ+ 482 age-0056 age²3.46 restaurn (.097) (.0009) (.80) (.120) n=807, R² = .1134.