StockWatson2007 {AER} | R Documentation

## Data and Examples from Stock and Watson (2007)

### Description

This manual page collects a list of examples from the book. Some solutions might not be exact and the list is certainly not complete. If you have suggestions for improvement (preferably in the form of code), please contact the package maintainer.

### References

Stock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.

### See Also

`CartelStability`, `CASchools`, `CigarettesSW`, `CollegeDistance`, `CPSSW04`, `CPSSW3`, `CPSSW8`, `CPSSW9298`, `CPSSW9204`, `CPSSWEducation`, `Fatalities`, `Fertility`, `Fertility2`, `FrozenJuice`, `GrowthSW`, `Guns`, `HealthInsurance`, `HMDA`, `Journals`, `MASchools`, `NYSESW`, `ResumeNames`, `SmokeBan`, `SportsCards`, `STAR`, `TeachingRatings`, `USMacroSW`, `USMacroSWM`, `USMacroSWQ`, `USSeatBelts`, `USStocksSW`, `WeakInstrument`

### Examples

    ###############################
    ## Current Population Survey ##
    ###############################

    ## p. 165
    data("CPSSWEducation", package = "AER")
    plot(earnings ~ education, data = CPSSWEducation)
    fm <- lm(earnings ~ education, data = CPSSWEducation)
    coeftest(fm, vcov = sandwich)
    abline(fm)

    ############################
    ## California test scores ##
    ############################

    ## data and transformations
    data("CASchools", package = "AER")
    CASchools$stratio <- with(CASchools, students/teachers)
    CASchools$score <- with(CASchools, (math + read)/2)

    ## p. 152
    fm1 <- lm(score ~ stratio, data = CASchools)
    coeftest(fm1, vcov = sandwich)

    ## p. 159
    fm2 <- lm(score ~ I(stratio < 20), data = CASchools)
    ## p. 199
    fm3 <- lm(score ~ stratio + english, data = CASchools)
    ## p. 224
    fm4 <- lm(score ~ stratio + expenditure + english, data = CASchools)

    ## Table 7.1, p. 242 (numbers refer to columns)
    fmc3 <- lm(score ~ stratio + english + lunch, data = CASchools)
    fmc4 <- lm(score ~ stratio + english + calworks, data = CASchools)
    fmc5 <- lm(score ~ stratio + english + lunch + calworks, data = CASchools)

    ## Equation 8.2, p. 258
    fmquad <- lm(score ~ income + I(income^2), data = CASchools)
    ## Equation 8.11, p. 266
    fmcub <- lm(score ~ income + I(income^2) + I(income^3), data = CASchools)
    ## Equation 8.23, p. 272
    fmloglog <- lm(log(score) ~ log(income), data = CASchools)
    ## Equation 8.24, p. 274
    fmloglin <- lm(log(score) ~ income, data = CASchools)
    ## Equation 8.26, p. 275
    fmlinlogcub <- lm(score ~ log(income) + I(log(income)^2) + I(log(income)^3),
      data = CASchools)

    ## Table 8.3, p. 292 (numbers refer to columns)
    fmc2 <- lm(score ~ stratio + english + lunch + log(income), data = CASchools)
    fmc7 <- lm(score ~ stratio + I(stratio^2) + I(stratio^3) + english + lunch + log(income),
      data = CASchools)

    #####################################
    ## Economics journal Subscriptions ##
    #####################################

    ## data and transformed variables
    data("Journals", package = "AER")
    journals <- Journals[, c("subs", "price")]
    journals$citeprice <- Journals$price/Journals$citations
    journals$age <- 2000 - Journals$foundingyear
    journals$chars <- Journals$charpp*Journals$pages/10^6

    ## Figure 8.9 (a) and (b)
    plot(subs ~ citeprice, data = journals, pch = 19)
    plot(log(subs) ~ log(citeprice), data = journals, pch = 19)
    fm1 <- lm(log(subs) ~ log(citeprice), data = journals)
    abline(fm1)

    ## Table 8.2, use HC1 for comparability with Stata
    fm1 <- lm(subs ~ citeprice, data = log(journals))
    fm2 <- lm(subs ~ citeprice + age + chars, data = log(journals))
    fm3 <- lm(subs ~ citeprice + I(citeprice^2) + I(citeprice^3) +
      age + I(age * citeprice) + chars, data = log(journals))
    fm4 <- lm(subs ~ citeprice + age + I(age * citeprice) + chars, data = log(journals))
    coeftest(fm1, vcov = vcovHC(fm1, type = "HC1"))
    coeftest(fm2, vcov = vcovHC(fm2, type = "HC1"))
    coeftest(fm3, vcov = vcovHC(fm3, type = "HC1"))
    coeftest(fm4, vcov = vcovHC(fm4, type = "HC1"))
    waldtest(fm3, fm4, vcov = vcovHC(fm3, type = "HC1"))

    ###############################
    ## Massachusetts test scores ##
    ###############################

    ## compare Massachusetts with California
    data("MASchools", package = "AER")
    data("CASchools", package = "AER")
    CASchools$stratio <- with(CASchools, students/teachers)
    CASchools$score4 <- with(CASchools, (math + read)/2)

    ## parts of Table 9.1, p. 330
    vars <- c("score4", "stratio", "english", "lunch", "income")
    cbind(
      CA_mean = sapply(CASchools[, vars], mean),
      CA_sd   = sapply(CASchools[, vars], sd),
      MA_mean = sapply(MASchools[, vars], mean),
      MA_sd   = sapply(MASchools[, vars], sd))

    ## Table 9.2, pp. 332--333, numbers refer to columns
    MASchools$higheng <- with(MASchools, english > median(english))
    fm1 <- lm(score4 ~ stratio, data = MASchools)
    fm2 <- lm(score4 ~ stratio + english + lunch + log(income), data = MASchools)
    fm3 <- lm(score4 ~ stratio + english + lunch + income + I(income^2) + I(income^3),
      data = MASchools)
    fm4 <- lm(score4 ~ stratio + I(stratio^2) + I(stratio^3) + english + lunch +
      income + I(income^2) + I(income^3), data = MASchools)
    fm5 <- lm(score4 ~ stratio + higheng + I(higheng * stratio) + lunch +
      income + I(income^2) + I(income^3), data = MASchools)
    fm6 <- lm(score4 ~ stratio + lunch + income + I(income^2) + I(income^3),
      data = MASchools)

    ## for comparability with Stata use HC1 below
    coeftest(fm1, vcov = vcovHC(fm1, type = "HC1"))
    coeftest(fm2, vcov = vcovHC(fm2, type = "HC1"))
    coeftest(fm3, vcov = vcovHC(fm3, type = "HC1"))
    coeftest(fm4, vcov = vcovHC(fm4, type = "HC1"))
    coeftest(fm5, vcov = vcovHC(fm5, type = "HC1"))
    coeftest(fm6, vcov = vcovHC(fm6, type = "HC1"))

    ## Testing exclusion of groups of variables
    fm3r <- update(fm3, . ~ . - I(income^2) - I(income^3))
    waldtest(fm3, fm3r, vcov = vcovHC(fm3, type = "HC1"))

    fm4r_str1 <- update(fm4, . ~ . - stratio - I(stratio^2) - I(stratio^3))
    waldtest(fm4, fm4r_str1, vcov = vcovHC(fm4, type = "HC1"))
    fm4r_str2 <- update(fm4, . ~ . - I(stratio^2) - I(stratio^3))
    waldtest(fm4, fm4r_str2, vcov = vcovHC(fm4, type = "HC1"))
    fm4r_inc <- update(fm4, . ~ . - I(income^2) - I(income^3))
    waldtest(fm4, fm4r_inc, vcov = vcovHC(fm4, type = "HC1"))

    fm5r_str <- update(fm5, . ~ . - stratio - I(higheng * stratio))
    waldtest(fm5, fm5r_str, vcov = vcovHC(fm5, type = "HC1"))
    fm5r_inc <- update(fm5, . ~ . - I(income^2) - I(income^3))
    waldtest(fm5, fm5r_inc, vcov = vcovHC(fm5, type = "HC1"))
    fm5r_high <- update(fm5, . ~ . - higheng - I(higheng * stratio))
    waldtest(fm5, fm5r_high, vcov = vcovHC(fm5, type = "HC1"))

    fm6r_inc <- update(fm6, . ~ . - I(income^2) - I(income^3))
    waldtest(fm6, fm6r_inc, vcov = vcovHC(fm6, type = "HC1"))

    ##################################
    ## Home mortgage disclosure act ##
    ##################################

    ## data
    data("HMDA", package = "AER")

    ## 11.1, 11.3, 11.7, 11.8 and 11.10, pp. 387--395
    fm1 <- lm(I(as.numeric(deny) - 1) ~ pirat, data = HMDA)
    fm2 <- lm(I(as.numeric(deny) - 1) ~ pirat + afam, data = HMDA)
    fm3 <- glm(deny ~ pirat, family = binomial(link = "probit"), data = HMDA)
    fm4 <- glm(deny ~ pirat + afam, family = binomial(link = "probit"), data = HMDA)
    fm5 <- glm(deny ~ pirat + afam, family = binomial(link = "logit"), data = HMDA)

    ## Table 11.1, p. 401
    mean(HMDA$pirat)
    mean(HMDA$hirat)
    mean(HMDA$lvrat)
    mean(as.numeric(HMDA$chist))
    mean(as.numeric(HMDA$mhist))
    mean(as.numeric(HMDA$phist)-1)
    prop.table(table(HMDA$insurance))
    prop.table(table(HMDA$selfemp))
    prop.table(table(HMDA$single))
    prop.table(table(HMDA$hschool))
    mean(HMDA$unemp)
    prop.table(table(HMDA$condomin))
    prop.table(table(HMDA$afam))
    prop.table(table(HMDA$deny))

    ## Table 11.2, pp. 403--404, numbers refer to columns
    HMDA$lvrat <- factor(ifelse(HMDA$lvrat < 0.8, "low",
      ifelse(HMDA$lvrat >= 0.8 & HMDA$lvrat <= 0.95, "medium", "high")),
      levels = c("low", "medium", "high"))
    HMDA$mhist <- as.numeric(HMDA$mhist)
    HMDA$chist <- as.numeric(HMDA$chist)

    fm1 <- lm(I(as.numeric(deny) - 1) ~ afam + pirat + hirat + lvrat + chist + mhist +
      phist + insurance + selfemp, data = HMDA)
    fm2 <- glm(deny ~ afam + pirat + hirat + lvrat + chist + mhist + phist + insurance +
      selfemp, family = binomial, data = HMDA)
    fm3 <- glm(deny ~ afam + pirat + hirat + lvrat + chist + mhist + phist + insurance +
      selfemp, family = binomial(link = "probit"), data = HMDA)
    fm4 <- glm(deny ~ afam + pirat + hirat + lvrat + chist + mhist + phist + insurance +
      selfemp + single + hschool + unemp, family = binomial(link = "probit"), data = HMDA)
    fm5 <- glm(deny ~ afam + pirat + hirat + lvrat + chist + mhist + phist + insurance +
      selfemp + single + hschool + unemp + condomin +
      I(mhist==3) + I(mhist==4) + I(chist==3) + I(chist==4) + I(chist==5) + I(chist==6),
      family = binomial(link = "probit"), data = HMDA)
    fm6 <- glm(deny ~ afam * (pirat + hirat) + lvrat + chist + mhist + phist + insurance +
      selfemp + single + hschool + unemp, family = binomial(link = "probit"), data = HMDA)
    coeftest(fm1, vcov = sandwich)

    fm4r <- update(fm4, . ~ . - single - hschool - unemp)
    waldtest(fm4, fm4r, vcov = sandwich)
    fm5r <- update(fm5, . ~ . - single - hschool - unemp)
    waldtest(fm5, fm5r, vcov = sandwich)
    fm6r <- update(fm6, . ~ . - single - hschool - unemp)
    waldtest(fm6, fm6r, vcov = sandwich)

    fm5r2 <- update(fm5, . ~ . - I(mhist==3) - I(mhist==4) - I(chist==3) - I(chist==4) -
      I(chist==5) - I(chist==6))
    waldtest(fm5, fm5r2, vcov = sandwich)

    fm6r2 <- update(fm6, . ~ . - afam * (pirat + hirat) + pirat + hirat)
    waldtest(fm6, fm6r2, vcov = sandwich)

    fm6r3 <- update(fm6, . ~ . - afam * (pirat + hirat) + pirat + hirat + afam)
    waldtest(fm6, fm6r3, vcov = sandwich)

    #########################################################
    ## Shooting down the "More Guns Less Crime" hypothesis ##
    #########################################################

    ## data
    data("Guns", package = "AER")

    ## Empirical Exercise 10.1
    fm1 <- lm(log(violent) ~ law, data = Guns)
    fm2 <- lm(log(violent) ~ law + prisoners + density + income +
      population + afam + cauc + male, data = Guns)
    fm3 <- lm(log(violent) ~ law + prisoners + density + income +
      population + afam + cauc + male + state, data = Guns)
    fm4 <- lm(log(violent) ~ law + prisoners + density + income +
      population + afam + cauc + male + state + year, data = Guns)
    coeftest(fm1, vcov = sandwich)
    coeftest(fm2, vcov = sandwich)
    printCoefmat(coeftest(fm3, vcov = sandwich)[1:9,])
    printCoefmat(coeftest(fm4, vcov = sandwich)[1:9,])

    ###########################
    ## US traffic fatalities ##
    ###########################

    ## data from Stock and Watson (2007)
    data("Fatalities")
    ## add fatality rate (number of traffic deaths
    ## per 10,000 people living in that state in that year)
    Fatalities$frate <- with(Fatalities, fatal/pop * 10000)
    ## add discretized version of minimum legal drinking age
    Fatalities$drinkagec <- cut(Fatalities$drinkage,
      breaks = 18:22, include.lowest = TRUE, right = FALSE)
    Fatalities$drinkagec <- relevel(Fatalities$drinkagec, ref = 4)
    ## any punishment?
    Fatalities$punish <- with(Fatalities,
      factor(jail == "yes" | service == "yes", labels = c("no", "yes")))
    ## plm package
    library("plm")

    ## for comparability with Stata we use HC1 below
    ## p. 351, Eq. (10.2)
    f1982 <- subset(Fatalities, year == "1982")
    fm_1982 <- lm(frate ~ beertax, data = f1982)
    coeftest(fm_1982, vcov = vcovHC(fm_1982, type = "HC1"))

    ## p. 353, Eq. (10.3)
    f1988 <- subset(Fatalities, year == "1988")
    fm_1988 <- lm(frate ~ beertax, data = f1988)
    coeftest(fm_1988, vcov = vcovHC(fm_1988, type = "HC1"))

    ## pp. 355, Eq. (10.8)
    fm_diff <- lm(I(f1988$frate - f1982$frate) ~ I(f1988$beertax - f1982$beertax))
    coeftest(fm_diff, vcov = vcovHC(fm_diff, type = "HC1"))

    ## pp. 360, Eq. (10.15)
    ##   (1) via formula
    fm_sfe <- lm(frate ~ beertax + state - 1, data = Fatalities)
    ##   (2) by hand
    fat <- with(Fatalities,
      data.frame(frates = frate - ave(frate, state),
      beertaxs = beertax - ave(beertax, state)))
    fm_sfe2 <- lm(frates ~ beertaxs - 1, data = fat)
    ##   (3) via plm()
    fm_sfe3 <- plm(frate ~ beertax, data = Fatalities,
      index = c("state", "year"), model = "within")

    coeftest(fm_sfe, vcov = vcovHC(fm_sfe, type = "HC1"))[1,]

    ## uses different df in sd and p-value
    coeftest(fm_sfe2, vcov = vcovHC(fm_sfe2, type = "HC1"))[1,]

    ## uses different df in p-value
    coeftest(fm_sfe3, vcov = vcovHC(fm_sfe3, type = "HC1", method = "white1"))[1,]

    ## pp. 363, Eq. (10.21)
    ## via lm()
    fm_stfe <- lm(frate ~ beertax + state + year - 1, data = Fatalities)
    coeftest(fm_stfe, vcov = vcovHC(fm_stfe, type = "HC1"))[1,]
    ## via plm()
    fm_stfe2 <- plm(frate ~ beertax, data = Fatalities,
      index = c("state", "year"), model = "within", effect = "twoways")
    coeftest(fm_stfe2, vcov = vcovHC) ## different

    ## p. 368, Table 10.1, numbers refer to cols.
    fm1 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"),
      model = "pooling")
    fm2 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"),
      model = "within")
    fm3 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"),
      model = "within", effect = "twoways")
    fm4 <- plm(frate ~ beertax + drinkagec + jail + service + miles + unemp + log(income),
      data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways")
    fm5 <- plm(frate ~ beertax + drinkagec + jail + service + miles,
      data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways")
    fm6 <- plm(frate ~ beertax + drinkage + punish + miles + unemp + log(income),
      data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways")
    fm7 <- plm(frate ~ beertax + drinkagec + jail + service + miles + unemp + log(income),
      data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways")
    ## summaries not too close, s.e.s generally too small
    coeftest(fm1, vcov = vcovHC)
    coeftest(fm2, vcov = vcovHC)
    coeftest(fm3, vcov = vcovHC)
    coeftest(fm4, vcov = vcovHC)
    coeftest(fm5, vcov = vcovHC)
    coeftest(fm6, vcov = vcovHC)
    coeftest(fm7, vcov = vcovHC)

    ######################################
    ## Cigarette consumption panel data ##
    ######################################

    ## data and transformations
    data("CigarettesSW", package = "AER")
    CigarettesSW$rprice <- with(CigarettesSW, price/cpi)
    CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi)
    CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi)
    c1985 <- subset(CigarettesSW, year == "1985")
    c1995 <- subset(CigarettesSW, year == "1995")

    ## convenience function: HC1 covariances
    hc1 <- function(x) vcovHC(x, type = "HC1")

    ## Equations 12.9--12.11
    fm_s1 <- lm(log(rprice) ~ tdiff, data = c1995)
    coeftest(fm_s1, vcov = hc1)
    fm_s2 <- lm(log(packs) ~ fitted(fm_s1), data = c1995)
    fm_ivreg <- ivreg(log(packs) ~ log(rprice) | tdiff, data = c1995)
    coeftest(fm_ivreg, vcov = hc1)

    ## Equation 12.15
    fm_ivreg2 <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff,
      data = c1995)
    coeftest(fm_ivreg2, vcov = hc1)
    ## Equation 12.16
    fm_ivreg3 <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff + I(tax/cpi),
      data = c1995)
    coeftest(fm_ivreg3, vcov = hc1)

    ## Table 12.1, p. 448
    ydiff <- log(c1995$packs) - log(c1985$packs)
    pricediff <- log(c1995$price/c1995$cpi) - log(c1985$price/c1985$cpi)
    incdiff <- log(c1995$income/c1995$population/c1995$cpi) -
      log(c1985$income/c1985$population/c1985$cpi)
    taxsdiff <- (c1995$taxs - c1995$tax)/c1995$cpi - (c1985$taxs - c1985$tax)/c1985$cpi
    taxdiff <- c1995$tax/c1995$cpi - c1985$tax/c1985$cpi

    fm_diff1 <- ivreg(ydiff ~ pricediff + incdiff | incdiff + taxsdiff)
    fm_diff2 <- ivreg(ydiff ~ pricediff + incdiff | incdiff + taxdiff)
    fm_diff3 <- ivreg(ydiff ~ pricediff + incdiff | incdiff + taxsdiff + taxdiff)
    coeftest(fm_diff1, vcov = hc1)
    coeftest(fm_diff2, vcov = hc1)
    coeftest(fm_diff3, vcov = hc1)

    ## checking instrument relevance
    fm_rel1 <- lm(pricediff ~ taxsdiff + incdiff)
    fm_rel2 <- lm(pricediff ~ taxdiff + incdiff)
    fm_rel3 <- lm(pricediff ~ incdiff + taxsdiff + taxdiff)
    linearHypothesis(fm_rel1, "taxsdiff = 0", vcov = hc1)
    linearHypothesis(fm_rel2, "taxdiff = 0", vcov = hc1)
    linearHypothesis(fm_rel3, c("taxsdiff = 0", "taxdiff = 0"),  vcov = hc1)

    ## testing overidentifying restrictions (J test)
    fm_or <- lm(residuals(fm_diff3) ~ incdiff + taxsdiff + taxdiff)
    (fm_or_test <- linearHypothesis(fm_or, c("taxsdiff = 0", "taxdiff = 0"), test = "Chisq"))
    ## warning: df (and hence p-value) invalid above.
    ## correct df: # instruments - # endogenous variables
    pchisq(fm_or_test[2,5], df.residual(fm_diff3) - df.residual(fm_or), lower.tail = FALSE)

    #####################################################
    ## Project STAR: Student-teacher achievement ratio ##
    #####################################################

    ## data
    data("STAR", package = "AER")

    ## p. 488
    fmk <- lm(I(readk + mathk) ~ stark, data = STAR)
    fm1 <- lm(I(read1 + math1) ~ star1, data = STAR)
    fm2 <- lm(I(read2 + math2) ~ star2, data = STAR)
    fm3 <- lm(I(read3 + math3) ~ star3, data = STAR)
    coeftest(fm3, vcov = sandwich)

    ## p. 489
    fmke <- lm(I(readk + mathk) ~ stark + experiencek, data = STAR)
    coeftest(fmke, vcov = sandwich)

    ## equivalently:
    ##   - reshape data from wide into long format
    ##   - fit a single model nested in grade
    ## (a) variables and their levels
    nam <- c("star", "read", "math", "lunch", "school", "degree", "ladder",
      "experience", "tethnicity", "system", "schoolid")
    lev <- c("k", "1", "2", "3")
    ## (b) reshaping
    star <- reshape(STAR, idvar = "id", ids = row.names(STAR),
      times = lev, timevar = "grade", direction = "long",
      varying = lapply(nam, function(x) paste(x, lev, sep = "")))
    ## (c) improve variable names and type
    names(star)[5:15] <- nam
    star$id <- factor(star$id)
    star$grade <- factor(star$grade, levels = lev,
      labels = c("kindergarten", "1st", "2nd", "3rd"))
    rm(nam, lev)
    ## (d) model fitting
    fm <- lm(I(read + math) ~ 0 + grade/star, data = star)

    #################################################
    ## Quarterly US macroeconomic data (1957-2005) ##
    #################################################

    ## data
    data("USMacroSW", package = "AER")
    library("dynlm")
    usm <- ts.intersect(USMacroSW, 4 * 100 * diff(log(USMacroSW[, "cpi"])))
    colnames(usm) <- c(colnames(USMacroSW), "infl")

    ## Equation 14.7, p. 536
    fm_ar1 <- dynlm(d(infl) ~ L(d(infl)),
      data = usm, start = c(1962,1), end = c(2004,4))
    coeftest(fm_ar1, vcov = sandwich)

    ## Equation 14.13, p. 538
    fm_ar4 <- dynlm(d(infl) ~ L(d(infl), 1:4),
      data = usm, start = c(1962,1), end = c(2004,4))
    coeftest(fm_ar4, vcov = sandwich)

    ## Equation 14.16, p. 542
    fm_adl41 <- dynlm(d(infl) ~ L(d(infl), 1:4) + L(unemp),
      data = usm, start = c(1962,1), end = c(2004,4))
    coeftest(fm_adl41, vcov = sandwich)

    ## Equation 14.17, p. 542
    fm_adl44 <- dynlm(d(infl) ~ L(d(infl), 1:4) + L(unemp, 1:4),
      data = usm, start = c(1962,1), end = c(2004,4))
    coeftest(fm_adl44, vcov = sandwich)

    ## Granger causality test mentioned on p. 547
    waldtest(fm_ar4, fm_adl44, vcov = sandwich)

    ## Equation 14.28, p. 559
    fm_sp1 <- dynlm(infl ~ log(gdpjp), start = c(1965,1), end = c(1981,4), data = usm)
    coeftest(fm_sp1, vcov = sandwich)

    ## Equation 14.29, p. 559
    fm_sp2 <- dynlm(infl ~ log(gdpjp), start = c(1982,1), end = c(2004,4), data = usm)
    coeftest(fm_sp2, vcov = sandwich)

    ## Equation 14.34, p. 563: ADF by hand
    fm_adf <- dynlm(d(infl) ~ L(infl) + L(d(infl), 1:4),
      data = usm, start = c(1962,1), end = c(2004,4))
    coeftest(fm_adf)

    ## Figure 14.5, p. 570
    ## SW perform partial break test of unemp coefs
    ## here full model is used
    library("strucchange")
    infl <- usm[, "infl"]
    unemp <- usm[, "unemp"]
    usm <- ts.intersect(diff(infl), lag(diff(infl), k = -1), lag(diff(infl), k = -2),
      lag(diff(infl), k = -3), lag(diff(infl), k = -4), lag(unemp, k = -1),
      lag(unemp, k = -2), lag(unemp, k = -3), lag(unemp, k = -4))
    colnames(usm) <- c("dinfl", paste("dinfl", 1:4, sep = ""), paste("unemp", 1:4, sep = ""))
    usm <- window(usm, start = c(1962, 1), end = c(2004, 4))
    fs <- Fstats(dinfl ~ ., data = usm)
    sctest(fs, type = "supF")
    plot(fs)

    ## alternatively: re-use fm_adl44
    mf <- model.frame(fm_adl44)
    mf <- ts(as.matrix(mf), start = c(1962, 1), freq = 4)
    colnames(mf) <- c("y", paste("x", 1:8, sep = ""))
    ff <- as.formula(paste("y", "~",  paste("x", 1:8, sep = "", collapse = " + ")))
    fs <- Fstats(ff, data = mf, from = 0.1)
    plot(fs)
    lines(boundary(fs, alpha = 0.01), lty = 2, col = 2)
    lines(boundary(fs, alpha = 0.1), lty = 3, col = 2)

    ##########################################
    ## Monthly US stock returns (1931-2002) ##
    ##########################################

    ## package and data
    library("dynlm")
    data("USStocksSW", package = "AER")

    ## Table 14.3, p. 540
    fm1 <- dynlm(returns ~ L(returns), data = USStocksSW, start = c(1960,1))
    coeftest(fm1, vcov = sandwich)
    fm2 <- dynlm(returns ~ L(returns, 1:2), data = USStocksSW, start = c(1960,1))
    waldtest(fm2, vcov = sandwich)
    fm3 <- dynlm(returns ~ L(returns, 1:4), data = USStocksSW, start = c(1960,1))
    waldtest(fm3, vcov = sandwich)

    ## Table 14.7, p. 574
    fm4 <- dynlm(returns ~ L(returns) + L(d(dividend)),
      data = USStocksSW, start = c(1960, 1))
    fm5 <- dynlm(returns ~ L(returns, 1:2) + L(d(dividend), 1:2),
      data = USStocksSW, start = c(1960, 1))
    fm6 <- dynlm(returns ~ L(returns) + L(dividend),
      data = USStocksSW, start = c(1960, 1))

    ##################################
    ## Price of frozen orange juice ##
    ##################################

    ## load data
    data("FrozenJuice")

    ## Stock and Watson, p. 594
    library("dynlm")
    fm_dyn <- dynlm(d(100 * log(price/ppi)) ~ fdd, data = FrozenJuice)
    coeftest(fm_dyn, vcov = vcovHC(fm_dyn, type = "HC1"))

    ## equivalently, returns can be computed 'by hand'
    ## (reducing the complexity of the formula notation)
    fj <- ts.union(fdd = FrozenJuice[, "fdd"],
      ret = 100 * diff(log(FrozenJuice[,"price"]/FrozenJuice[,"ppi"])))
    fm_dyn <- dynlm(ret ~ fdd, data = fj)

    ## Stock and Watson, p. 595
    fm_dl <- dynlm(ret ~ L(fdd, 0:6), data = fj)
    coeftest(fm_dl, vcov = vcovHC(fm_dl, type = "HC1"))

    ## Stock and Watson, Table 15.1, p. 620, numbers refer to columns
    ## (1) Dynamic Multipliers
    fm1 <- dynlm(ret ~ L(fdd, 0:18), data = fj)
    coeftest(fm1, vcov = NeweyWest(fm1, lag = 7, prewhite =  FALSE))
    ## (2) Cumulative Multipliers
    fm2 <- dynlm(ret ~ L(d(fdd), 0:17) + L(fdd, 18), data = fj)
    coeftest(fm2, vcov = NeweyWest(fm2, lag = 7, prewhite =  FALSE))
    ## (3) Cumulative Multipliers, more lags in NW
    coeftest(fm2, vcov = NeweyWest(fm2, lag = 14, prewhite =  FALSE))
    ## (4) Cumulative Multipliers with monthly indicators
    fm4 <- dynlm(ret ~ L(d(fdd), 0:17) + L(fdd, 18) + season(fdd), data = fj)
    coeftest(fm4, vcov = NeweyWest(fm4, lag = 7, prewhite =  FALSE))
    ## monthly indicators needed?
    fm4r <- update(fm4, . ~ . - season(fdd))
    waldtest(fm4, fm4r, vcov= NeweyWest(fm4, lag = 7, prewhite = FALSE)) ## close ...

    #############################################
    ## New York Stock Exchange composite index ##
    #############################################

    ## returns
    data("NYSESW", package = "AER")
    ret <- 100 * diff(log(NYSESW))
    plot(ret)

    ## fit GARCH(1,1)
    library("tseries")
    fm <- garch(coredata(ret))

[Package AER version 1.2-5 Index]