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2.2 Panel data models  (Page 10/10)

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The data you will use in this project are in the MS Excel file Smkdata.xls . The variables included in the file are as follows:

Definition of the cigarette consumption data set.
Column Variable Definition
A Country Name of country
B Country ID Integar from 1 to 22, each designating a country
C Year Year of observation (1964, …, 1990)
D Tobacco Total grams of tobacco sold per individual 15 years or older
E Price Real price of 20 grams of tobacco in 1990 US cents (= Nominal price per E 20 grams of tobacco divided by the Gross Domestic Price deflator)
F Consump Per capita private final consumption expenditures in 1990 US dollars
G Unemp Number of unemployed persons per 1000 members of the workforce
H AgeDist Age distribution. This variable attempts to measure the differences in intensity of smoking as a function of age. It is equal to the relative consumption rate of tobacco in the UK observed between 1966 and 1981 by age group times the percentage of the population in the country in that age group.
I Ban Dummy variable equal to 1 if the country has a complete ban on tobacco advertising. The six countries in the sample with a complete ban and the first year of the ban are: Iceland (1972), Norway (1976), Finland (1979), Portugal (1984), Italy (1984), and Canada (1989).
J BanTime The number of years since the ban was put in place (if ban went into effect in 1972, then years 1964-1972 are equal to 0, year 1973 equals 1, year 1974 equals 2, etc.)

(a) How do these variables match the ones suggested in the discussion of equations (1), (2), and (3)?

(b) Estimate the fixed effects models of the following versions of equations (1), (2), and (3):

  1. Equations (1), (2), and (3) as specified above.
  2. Equations (1) and (2) with squared terms for the price, income, unemployment rate, and the age distribution included. This regression is designed to test for non-linearity.
  3. Equations (1) and (2) with the squared terms mentioned in 2 that are statistically significant plus the following new variables: Ban*Time, Ban*Price, and Ban*Consump. (You must create these variables) This regression allows for an effect of the Ban on the slopes of the other explanatory variables.
  4. Equation (3) with the following new variables: Ban*Log(Time), Ban*Log(Price), and Ban*Log(Consump).
  5. Equations (1), (2), and (3) as estimated in 3 and 4 with a variable that counts the number of years that a total ban has been in effect (BanTime) and its square (BanTime 2 ). This regression allows for a changing impact of a ban the longer it is in effect.

Report the results of your regressions in a table that allows you to comment on the stability of your estimation results over specifications.

(c) Do these results support any of the theories suggested above?

(d) What, if any, policy conclusions would you make given your estimations?

(e) Assume for the moment that you “believe” your results you got in (5). Sketch out a strategy you would follow to forecast the impact of a ban in a country that does not currently have a ban.

Note: The data in this problem are from Stewart, Michael J. (1993) “The Effect on Tobacco Consumption of Advertising Bans in OECD Countries,” International Journal of Advertising 12 (2): 155-180. The data set can be downloaded from the author's website .

Bibliography

Cameron, A. Colin and Pravin K. Trivedi (2005). Microeconometrics: Methods and Applications (New York: Cambridge University Press).

Greene, W. H. (2003). Econometric Analysis , 5 th edition (Upper Saddle River, NJ: Prentice-Hall).

Hsiao, Cheng (2003). Analysis of Panel Data , 2 nd Edition (New York: Cambridge University Press).

StataCorp (2003). Stata Statistical Software: Release8.0 (College Station, TX: Stata Corporation).

Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data (Cambridge, MA: MIT Press).

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Source:  OpenStax, Econometrics for honors students. OpenStax CNX. Jul 20, 2010 Download for free at http://cnx.org/content/col11208/1.2
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