- The variable smokes is a binary variable equal to one if a person smokes, and zero otherwise. A researcher has estimated the following linear probability model for smokes: smokes = .656 - .069log(cigpric) + .012 log(income) - .029 educ (.006) [.006] (.855) (.204) [.856] 1.207] (.026) [.026] (.006) [.005] +.020 age - 0.00026age2- 0.101 restaurn - .026white (.039) [.038] (.00006) 1.00006] (.052) [.050] n=807, R2 = 0.062. Standard errors are provided in (.) and heteroskedasticity adjusted standard errors are provided in [.]. • income= annual income in US dollars • cigprice= the per-pack price of cigarettes (in cents) • educ= years of schooling • age= age measured in years • restauran= a binary indicator equal to unity if the person resides in a state with restaurant smoking restrictions • white= a dummy for race. white=1 for white and zero otherwise Analyse this model using all information available to you. Provide an effective policy discussion of this model.

A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
Section: Chapter Questions
Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
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This is an econometrics question please help me solve it. 

- The variable smokes is a binary variable equal to one if a
person smokes, and zero otherwise. A researcher has
estimated the following linear probability model for smokes:
smokes = .656 - .069log(cigpric) + .012 log(income) – .029 educ
(.006)
[.006]
(.855) (.204)
[.856] 1.207]
(.026)
[.026]
(.006)
[.005]
+.020 age - 0.00026age2- 0.101 restaurn - .026white
(.039)
[.038]
.00006)
1.00006]
(.052)
[.050]
n=807, R2 = 0.062. Standard errors are provided in (.) and
heteroskedasticity adjusted standard errors are provided in [.].
• income= annual income in US dollars
• cigprice= the per-pack price of cigarettes (in cents)
• educ= years of schooling
• age= age measured in years
• restauran= a binary indicator equal to unity if the person
resides in a state with restaurant smoking restrictions
• white= a dummy for race. white=1 for white and zero
otherwise
Analyse this model using all information available to you.
Provide an effective policy discussion of this model.
Transcribed Image Text:- The variable smokes is a binary variable equal to one if a person smokes, and zero otherwise. A researcher has estimated the following linear probability model for smokes: smokes = .656 - .069log(cigpric) + .012 log(income) – .029 educ (.006) [.006] (.855) (.204) [.856] 1.207] (.026) [.026] (.006) [.005] +.020 age - 0.00026age2- 0.101 restaurn - .026white (.039) [.038] .00006) 1.00006] (.052) [.050] n=807, R2 = 0.062. Standard errors are provided in (.) and heteroskedasticity adjusted standard errors are provided in [.]. • income= annual income in US dollars • cigprice= the per-pack price of cigarettes (in cents) • educ= years of schooling • age= age measured in years • restauran= a binary indicator equal to unity if the person resides in a state with restaurant smoking restrictions • white= a dummy for race. white=1 for white and zero otherwise Analyse this model using all information available to you. Provide an effective policy discussion of this model.
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