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Statistics – Supply and Demand Task

Solution for Statistics – Supply and Demand Task

(a) β̂1 = −0.75317
Confidence interval is: ( −0.8050502, −0.7012837 )
(b) For a variable to be valid instrument for log_p , it should be correlated with log_p but
uncorrelated with error term (UI
)

For this data
Cov(log _ p , distance _ oil1000) = 0.03071227
and Cov(log _ p , log _ q) = −3.55e − 19~0
Hence distance _ oil1000 is a valid instrument
(c) If we regress log_p on distance_oil1000 and perform t-test on the mean of error term the
following output is obtained:

This indicates we accept null hypothesis
(d) β̂1 = −1.20575
Confidence interval is: ( −1.295918, −0.663003 )
(e) OLS Regression: β̂1 = −0.98471
Confidence interval is: ( −1.032832, −0.9365815 )
IV Regression: β̂1 = −1.63115
Confidence interval is: ( −1.713983, −1.548315

 

library(foreign)
data=read.dta(“gasoline_demand_BHP2012.dta”)
new_data=data[-which(is.na(data$distance_oil1000)==TRUE),]
#OLS
model=lm(log_q~log_p,data=new_data)
s=summary(model)
#HC standard error
hc_error=vcovHC(model,type=”HC”)

#conf interval
lower=s$coefficients[2]-(1.96*hc_error[2,2])
upper=s$coefficients[2]+(1.96*hc_error[2,2])

#instrument
error=new_data$log_q-model$fitted.values

#checking covariance condition for instruement variable
cov(error,new_data$log_p)
cov(new_data$distance_oil1000,new_data$log_p)

#regressing on instrument variable
m=lm(log_p~distance_oil1000,data=new_data,x=TRUE)
t.test(new_data$log_p,new_data$distance_oil1000)

#iv regression
library(AER)
library(ivpack)
ivmodel=ivreg(log_q~log_p|distance_oil1000,data=new_data,x=TRUE)
s1=summary(ivmodel)
#HC Standard error
hc_error1=vcovHC(ivmodel,type=”HC”)

#conf interval
lower1=s1$coefficients[2]-(1.96*hc_error1[2,2])
upper1=s$coefficients[2]+(1.96*hc_error1[2,2])
#setup 2
#OLS
mod=lm(log_q~log_p+log_y, data=new_data)
s_mod=summary(mod)
#HC Standard error
hc_mod=vcovHC(mod,type=”HC”)
#confidence interval
lower_mod=s_mod$coefficients[2]-1.96*hc_mod[2,2]
upper_mod=s_mod$coefficients[2]+1.96*hc_mod[2,2]

#IV
ivmod=ivreg(log_q~log_p+log_y|distance_oil1000+log_y,data=new_data,x=TRUE)
s_ivmod=summary(ivmod)
#HC Standard error
hc_ivmod=vcovHC(ivmod,type=”HC”)
#confidence interval
lower_ivmod=s_ivmod$coefficients[2]-1.96*hc_ivmod[2,2]
upper_ivmod=s_ivmod$coefficients[2]+1.96*hc_ivmod[2,2]