[SOLVED] CS代考计算机代写 ## —- warning=FALSE, message=FALSE, eval=TRUE, echo=FALSE, purl=TRUE—————————–

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# load the data set and get an overview
library(“AER”)
data(“CigarettesSW”)
summary(CigarettesSW)

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# compute real per capita prices
CigarettesSW$rprice <- with(CigarettesSW, price / cpi)#compute the sales taxCigarettesSW$salestax <- with(CigarettesSW, (taxs – tax) / cpi)# check the correlation between sales tax and pricecor(CigarettesSW$salestax, CigarettesSW$price)# generate a subset for the year 1995c1995 <- subset(CigarettesSW, year == “1995”)## ————————————————————————————————# perform the first stage regressioncig_s1 <- lm(log(rprice) ~ salestax, data = c1995)coeftest(cig_s1, vcov = vcovHC, type = “HC1”)## ————————————————————————————————# inspect the R^2 of the first stage regressionsummary(cig_s1)$r.squared## ————————————————————————————————# store the predicted valueslcigp_pred <- cig_s1$fitted.values## ————————————————————————————————# run the stage 2 regressioncig_s2 <- lm(log(c1995$packs) ~ lcigp_pred)coeftest(cig_s2, vcov = vcovHC)## ————————————————————————————————# perform TSLS using ‘ivreg()’cig_ivreg <- ivreg(log(packs) ~ log(rprice) | salestax,data = c1995)coeftest(cig_ivreg, vcov = vcovHC, type = “HC1”)## ————————————————————————————————# add real income to the dataset (cpi: consumer price index)CigarettesSW$rincome <- with(CigarettesSW,income / population / cpi)c1995 <- subset(CigarettesSW, year == “1995”)## ————————————————————————————————# estimate the modelcig_ivreg2 <- ivreg(log(packs) ~ log(rprice) +log(rincome) | log(rincome) + salestax, data = c1995)coeftest(cig_ivreg2, vcov = vcovHC, type = “HC1”)## ————————————————————————————————# add cigtax to the data setCigarettesSW$cigtax <- with(CigarettesSW, tax/cpi)c1995 <- subset(CigarettesSW, year == “1995”)## ————————————————————————————————# estimate the modelcig_ivreg3 <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + salestax + cigtax, data = c1995)coeftest(cig_ivreg3, vcov = vcovHC, type = “HC1”)## ————————————————————————————————# subset data for year 1985c1985 <- subset(CigarettesSW, year == “1985”)# define differences in variablespacksdiff <- log(c1995$packs) – log(c1985$packs)pricediff <- log(c1995$price/c1995$cpi) – log(c1985$price/c1985$cpi)incomediff <- log(c1995$income/c1995$population/c1995$cpi) -log(c1985$income/c1985$population/c1985$cpi)salestaxdiff <- (c1995$taxs – c1995$tax)/c1995$cpi – (c1985$taxs – c1985$tax)/c1985$cpicigtaxdiff <- c1995$tax/c1995$cpi – c1985$tax/c1985$cpi## ————————————————————————————————# estimate the three modelscig_ivreg_diff1 <- ivreg(packsdiff ~ pricediff +incomediff | incomediff +salestaxdiff)cig_ivreg_diff2 <- ivreg(packsdiff ~ pricediff +incomediff | incomediff +cigtaxdiff)cig_ivreg_diff3 <- ivreg(packsdiff ~ pricediff +incomediff | incomediff +salestaxdiff + cigtaxdiff)## ————————————————————————————————# robust coefficient summary for 1.coeftest(cig_ivreg_diff1, vcov = vcovHC, type = “HC1”)# robust coefficient summary for 2.coeftest(cig_ivreg_diff2, vcov = vcovHC, type = “HC1”)# robust coefficient summary for 3.coeftest(cig_ivreg_diff3, vcov = vcovHC, type = “HC1”)## —- eval = FALSE, echo=TRUE——————————————————————–## # gather robust standard errors in a list## rob_se <- list(sqrt(diag(vcovHC(cig_ivreg_diff1, type = “HC1”))),##sqrt(diag(vcovHC(cig_ivreg_diff2, type = “HC1”))),##sqrt(diag(vcovHC(cig_ivreg_diff3, type = “HC1”))))## ## # generate table## stargazer(cig_ivreg_diff1, cig_ivreg_diff2, cig_ivreg_diff3,## header = FALSE,## type = “latex”,## omit.table.layout = “n”,## digits = 3,## column.labels = c(“IV: salestax”, “IV: cigtax”,## “IVs: salestax, cigtax”),## dep.var.labels.include = FALSE,## dep.var.caption =## “Dependent Variable: 1985-1995 Difference in Log per Pack Price”,## se = rob_se)## ————————————————————————————————# first-stage regressionsmod_relevance1 <- lm(pricediff ~ salestaxdiff + incomediff)mod_relevance2 <- lm(pricediff ~ cigtaxdiff + incomediff)mod_relevance3 <- lm(pricediff ~ incomediff + salestaxdiff + cigtaxdiff)## ————————————————————————————————# check instrument relevance for model (1)linearHypothesis(mod_relevance1,”salestaxdiff = 0″,vcov = vcovHC, type = “HC1″)## ————————————————————————————————# check instrument relevance for model (2)linearHypothesis(mod_relevance2,”cigtaxdiff = 0”,vcov = vcovHC, type = “HC1”)## ————————————————————————————————# check instrument relevance for model (3)linearHypothesis(mod_relevance3,c(“salestaxdiff = 0”, “cigtaxdiff = 0”),vcov = vcovHC, type = “HC1”)## ————————————————————————————————# compute the J-statisticcig_iv_OR <- lm(residuals(cig_ivreg_diff3) ~ incomediff +salestaxdiff + cigtaxdiff)cig_OR_test <- linearHypothesis(cig_iv_OR,c(“salestaxdiff = 0″,”cigtaxdiff = 0”),test = “Chisq”)cig_OR_test## ————————————————————————————————# compute correct p-value for J-statisticpchisq(cig_OR_test[2, 5], df = 1, lower.tail = FALSE)