econ3208
ECON 3208/3291
Econometric Methods
Tutorial 1
(tutorials to be held in week 2)
The purpose of the following problem is to review some of the essential material covered in the Intro course.
In an attempt to better understand the determinants of tobacco demand, consider the following specification of a demand function for daily cigarette consumption:
CIGSi = β1 + β2 LINCOMEi + β3 LPRICEi + β4 EDUCi + β5 AGEi + β6 AGESQi + ui where i denotes individuals. This model is estimated using a sample of 806 individuals. Data definitions and associated summary statistics are given in Table 2.1 while the OLS regression results are given in Table 2.2.
Please answer the following questions using the information in the tables.
(i) Using the estimated results:
(a) Carefully interpret all of the parameters in the regression model including their magnitudes and expected and actual signs.
(b) Comment on the statistical significance of each of the estimated coefficients cigarette demand. (d) Suppose the regression model was re-estimated under the hypothesis that β2 = β3 = 0 to yield a residual sums of squares of 144,240. If the original model had a residual sums of squares of 144,000 test the hypothesis that β2 = β3 = 0. Does the result of this test affect your conclusion in (c)?
(e) On the basis of the results comment on the role of age as a determinant of cigarette demand. (f) Notice that there are no variables relating to government policy interventions such as prohibition of tobacco advertising or inclusion of health warnings on cigarette packets.
Provide one justification why this omission could have been a reasonable assumption for these data.
(ii) Suppose the following diagnostics were associated with this model
R2 = 0.20, RESET = 2.03, B-P = 25.63 where the RESET test uses both squared and cubed predictions as additional variables and BP is the LM version Breusch-Pagan test (see Wooldridge p. 269 or p. 277 in 3rd ed.) for the null hypothesis that
Econometric Methods
Tutorial 1
(tutorials to be held in week 2)
The purpose of the following problem is to review some of the essential material covered in the Intro course.
In an attempt to better understand the determinants of tobacco demand, consider the following specification of a demand function for daily cigarette consumption:
CIGSi = β1 + β2 LINCOMEi + β3 LPRICEi + β4 EDUCi + β5 AGEi + β6 AGESQi + ui where i denotes individuals. This model is estimated using a sample of 806 individuals. Data definitions and associated summary statistics are given in Table 2.1 while the OLS regression results are given in Table 2.2.
Please answer the following questions using the information in the tables.
(i) Using the estimated results:
(a) Carefully interpret all of the parameters in the regression model including their magnitudes and expected and actual signs.
(b) Comment on the statistical significance of each of the estimated coefficients cigarette demand. (d) Suppose the regression model was re-estimated under the hypothesis that β2 = β3 = 0 to yield a residual sums of squares of 144,240. If the original model had a residual sums of squares of 144,000 test the hypothesis that β2 = β3 = 0. Does the result of this test affect your conclusion in (c)?
(e) On the basis of the results comment on the role of age as a determinant of cigarette demand. (f) Notice that there are no variables relating to government policy interventions such as prohibition of tobacco advertising or inclusion of health warnings on cigarette packets.
Provide one justification why this omission could have been a reasonable assumption for these data.
(ii) Suppose the following diagnostics were associated with this model
R2 = 0.20, RESET = 2.03, B-P = 25.63 where the RESET test uses both squared and cubed predictions as additional variables and BP is the LM version Breusch-Pagan test (see Wooldridge p. 269 or p. 277 in 3rd ed.) for the null hypothesis that