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)
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)
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.6: Summarizing Categorical Data
Problem 23PPS
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