| Journals | R Documentation |
Subscriptions to economics journals at US libraries, for the year 2000.
data("Journals")A data frame containing 180 observations on 10 variables.
Journal title.
factor with publisher name.
factor. Is the journal published by a scholarly society?
Library subscription price.
Number of pages.
Characters per page.
Total number of citations.
Year journal was founded.
Number of library subscriptions.
factor with field description.
Data on 180 economic journals, collected in particular for analyzing journal pricing. See also https://econ.ucsb.edu/~tedb/Journals/jpricing.html for general information on this topic as well as a more up-to-date version of the data set. This version is taken from Stock and Watson (2007).
The data as obtained from the online complements for Stock and Watson (2007) contained two journals with title “World Development”. One of these (observation 80) seemed to be an error and was changed to “The World Economy”.
Online complements to Stock and Watson (2007).
Bergstrom, T. (2001). Free Labor for Costly Journals? Journal of Economic Perspectives, 15, 183–198.
Stock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.
StockWatson2007
## data and transformed variables
data("Journals")
journals <- Journals[, c("subs", "price")]
journals$citeprice <- Journals$price/Journals$citations
journals$age <- 2000 - Journals$foundingyear
journals$chars <- Journals$charpp*Journals$pages/10^6
## Stock and Watson (2007)
## 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
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"))
## changes with respect to age
library("strucchange")
## Nyblom-Hansen test
scus <- gefp(subs ~ citeprice, data = log(journals), fit = lm, order.by = ~ age)
plot(scus, functional = meanL2BB)
## estimate breakpoint(s)
journals <- journals[order(journals$age),]
bp <- breakpoints(subs ~ citeprice, data = log(journals), h = 20)
plot(bp)
bp.age <- journals$age[bp$breakpoints]
## visualization
plot(subs ~ citeprice, data = log(journals), pch = 19, col = (age > log(bp.age)) + 1)
abline(coef(bp)[1,], col = 1)
abline(coef(bp)[2,], col = 2)
legend("bottomleft", legend = c("age > 18", "age < 18"), lty = 1, col = 2:1, bty = "n")