Bibliographic information about papers related to structural change and changepoints published in 27 different econometrics and statistics journals.
A data frame containing information on 835 structural change papers in 9 variables.
character. Author(s) of the paper.
character. Title of the paper.
factor. In which journal was the paper published?
numeric. Year of publication.
numeric. Journal volume.
character. Issue within the journal volume.
numeric. Page on which the paper begins.
numeric. Page on which the paper ends.
factor. Is the journal an econometrics or statistics journal?
The data set
bibliographic information about publications related to structural change and obtained
from the ‘ISI Web of Science’. The query was based on the ‘Science Citation Index Expanded’
and ‘Social Sciences Citation Index’ (for the full range of years available: 1900-2006 and
1956-2006, respectively). The ‘Source Title’ was restricted to the 27 journals
in the data frame and the ‘Topic’ to be one of the following:
structural change, structural break, structural stability, structural instability,
parameter instability, parameter stability, parameter constancy, change point,
changepoint, change-point, breakpoint, break-point, break point, CUSUM, MOSUM.
Additionally, the famous CUSUM paper of Brown, Durbin and Evans (1975) was added
scPublications (because it did not match the query above).
ISI Web of Science at http://www.webofknowledge.com/. Queried by James Bullard.
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## construct time series: ## number of sc publications in econometrics/statistics data("scPublications") ## select years from 1987 and ## `most important' journals pub <- scPublications pub <- subset(pub, year > 1986) tab1 <- table(pub$journal) nam1 <- names(tab1)[as.vector(tab1) > 9] ## at least 10 papers tab2 <- sapply(levels(pub$journal), function(x) min(subset(pub, journal == x)$year)) nam2 <- names(tab2)[as.vector(tab2) < 1991] ## started at least in 1990 nam <- nam1[nam1 %in% nam2] pub <- subset(pub, as.character(journal) %in% nam) pub$journal <- factor(pub$journal) pub_data <- pub ## generate time series pub <- with(pub, tapply(type, year, table)) pub <- zoo(t(sapply(pub, cbind)), 1987:2006) colnames(pub) <- levels(pub_data$type) ## visualize plot(pub, ylim = c(0, 35))
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