strucchange (version 1.5-4)

scPublications: Structural Change Publications

Description

Bibliographic information about papers related to structural change and changepoints published in 27 different econometrics and statistics journals.

Usage

data("scPublications")

Format

A data frame containing information on 835 structural change papers in 9 variables.

author

character. Author(s) of the paper.

title

character. Title of the paper.

journal

factor. In which journal was the paper published?

year

numeric. Year of publication.

volume

numeric. Journal volume.

issue

character. Issue within the journal volume.

bpage

numeric. Page on which the paper begins.

epage

numeric. Page on which the paper ends.

type

factor. Is the journal an econometrics or statistics journal?

Details

The data set scPublications includes 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 manually to scPublications (because it did not match the query above).

Examples

## 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))