The Norwegian gover ts to know w restricting the opening hours of liquor stores reduces alcohol consumption. Holger, an employee of Statistics Norway, is asked to investigate this research question. He uses panel data for n = 60 municipalities observed in T = 10 time periods. The data set contains information on per capita alcohol consumption (in liters per year) in municipality i in year t (alcoholit) and on the number of hours that liquor stores were open during year t in municipality i (hoursit). Holger estimates alcoholit po + Biln(hoursit) + Uit by OLS and obtains the following estimation results. . regress alcohol Inhours, robust Linear regression alcohol Inhours cons Coef. 5.288235 2.768777 Robust Std. Err. .7486464 5.686286 t 0.49 P>|t| Explain how you could estimate model (1). Number of obs = 0.626 R-squared Root MSE alcoholit = Bo+piln(hoursit)+a+Uit [95% Conf. Interval] -8.398742 a) Test the null hypothesis that B₁ = 0 at a 1% significance level. b) Use the above estimation results to predict the change in alcohol consumpti if the opening hours of liquor stores are reduced by 20 percent. c) Marit, Holger's colleague, suggests augmenting the model with municipality fixed effects (a) 0.0745 .9568 600 13.9363 (1)

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The Norwegian government wants to know whether restricting the opening hours of liquor
stores reduces alcohol consumption. Holger, an employee of Statistics Norway, is asked to
investigate this research question. He uses panel data for n = 60 municipalities observed in T
= 10 time periods. The data set contains information on per capita alcohol consumption
(in liters per year) in municipality i in year t (alcoholit) and on the number of hours that
liquor stores were open during year t in municipality i (hoursit). Holger estimates
by OLS and obtains the following estimation results.
regress alcohol Inhours, robust
Linear regression
alcohol
alcoholit Bo + ßiln(hoursit) + Uit
B
Inhours
cons
Coef.
5.288235
2.768777
Robust
Std. Err.
.7486464
5.686286
t
0.49
P>|t|
Number of obs =
0.626
R-squared
Root MSE
alcoholit = Bo+piln(hoursit) + ai + Uit
[95% Conf. Interval]
-8.398742
0.0745
.9568
a) Test the null hypothesis that B₁ = 0 at a 1% significance level.
b)
Use the above estimation results to predict the change in alcohol consumption if the
opening hours of liquor stores are reduced by 20 percent.
c) Marit, Holger's colleague, suggests augmenting the model with municipality fixed
effects (a)
600
13.9363
(1)
Explain how you could estimate model (1).
d) Holger decides to estimate a model that includes both municipality and year fixed
effects. Both Holger and Marit are confident that by including municipality and year
fixed effects the estimated coefficient on In(hoursit) cannot suffer from omit- ted
variable bias problems. Do you agree with Holger and Marit?
Transcribed Image Text:The Norwegian government wants to know whether restricting the opening hours of liquor stores reduces alcohol consumption. Holger, an employee of Statistics Norway, is asked to investigate this research question. He uses panel data for n = 60 municipalities observed in T = 10 time periods. The data set contains information on per capita alcohol consumption (in liters per year) in municipality i in year t (alcoholit) and on the number of hours that liquor stores were open during year t in municipality i (hoursit). Holger estimates by OLS and obtains the following estimation results. regress alcohol Inhours, robust Linear regression alcohol alcoholit Bo + ßiln(hoursit) + Uit B Inhours cons Coef. 5.288235 2.768777 Robust Std. Err. .7486464 5.686286 t 0.49 P>|t| Number of obs = 0.626 R-squared Root MSE alcoholit = Bo+piln(hoursit) + ai + Uit [95% Conf. Interval] -8.398742 0.0745 .9568 a) Test the null hypothesis that B₁ = 0 at a 1% significance level. b) Use the above estimation results to predict the change in alcohol consumption if the opening hours of liquor stores are reduced by 20 percent. c) Marit, Holger's colleague, suggests augmenting the model with municipality fixed effects (a) 600 13.9363 (1) Explain how you could estimate model (1). d) Holger decides to estimate a model that includes both municipality and year fixed effects. Both Holger and Marit are confident that by including municipality and year fixed effects the estimated coefficient on In(hoursit) cannot suffer from omit- ted variable bias problems. Do you agree with Holger and Marit?
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