# Example data from Franses (1996) A multivariate time series containing the data used in examples by Franses (1996). data ```r data("dataFranses1996") ``` ## Format A multivariate quarterly time series. ## Details Each column is a quarterly time series. The time series start and end at different times, so `NA`'s are used to align them in a single multivariate time series. Detailed account of the sources of the data is given by \insertCite @@FransesB1; Data Appendix, p. 214;textualpcts. - **`year`**: (column 1) The time formatted as `yyyy.Q`, where `yyyy` is the year and `Q` is the quarter (one of 1, 2, 3 or 4.). This column was part of the original data but is not really needed here since the time series object contains the time information. - **`USTotalIPI`**: (column 2) Total Industrial Production Index for the United States (1985 = 100), 1960.1--1991.4. - **`CanadaUnemployment`**: (column 3) Unemployment in Canada, measured in 1000 persons, 1960.1 - 1987.4. - **`GermanyGNP`**: (column 4) Real GNP in Germany, 1960.1 - 1990.4 . - **`UKTotalInvestment`**: (column 5) Real Total Investment in the United Kindom, 1955.1 - 1988.4. - **`SA_USTotalIPI`**: (column 6) Seasonally adjusted `USTotalIPI`. - **`SA_CanadaUnemployment`**: (column 7) Seasonally adjusted `CanadaUnemployment`. - **`SA_GermanyGNP`**: (column 8) Seasonally adjusted `GermanyGNP`. - **`UKGDP`**: (column 9) United Kingdom gross domestic product (at 1985 prices), 1955.1--1988.4. - **`UKTotalConsumption`**: (column 10) United Kingdom total consumption (at 1985 prices), 1955.1--1988.4. - **`UKNondurablesConsumption`**: (column 11) United Kindom nondurables consumption (at 1985 prices), 1955.1--1988.4. - **`UKExport`**: (column 12) United Kindom exports of goods and services (at 1985 prices), 1955.1--1988.4. - **`UKImport`**: (column 13) United Kindom imports of goods and services (at 1985 prices), 1955.1--1988.4. - **`UKPublicInvestment`**: (column 14) United Kindom public investment (at 1985 prices), 1962.1--1988.4. - **`UKWorkforce`**: (column 15) United Kindom workforce (consisting of workforce in employment and unemployment), 1955.1--1988.4. - **`SwedenNondurablesConsumption`**: (column 16) Real per capita non-durables consumption in Sweden (measured in logs), 1963.1--1988.1. - **`SwedenDisposableIncome`**: (column 17) Real per capita disposable income in Sweden (measured in logs), 1963.1--1988.1. - **`SA_SwedenNondurablesConsumption`**: (column 18) Seasonally adjusted `SwedenNondurablesConsumption` with Census X-11 method, 1964.1--1988.1. (Using the approximate linear Census X-11 filter given in Table 4.1, p. 52 in \insertCite FransesB1;textualpcts and generating the forecasts and backcasts as described in Ooms (1994)). - **`SA_SwedenDisposableIncome`**: (column 19) Seasonally adjusted `SwedenDisposableIncome` with Census X-11 method, 1964.1--1988.1. (Using the same method as above.) More details on the individual time series are given by \insertCite FransesB1;textualpcts. ## Source The data were downloaded from `http://people.few.eur.nl/franses/research/data/data1.txt`, but this link is now broken. ## Note Most of the time series in `dataFranses1996` are available as separate datasets in package `partsm'. The numbers should be the same but note that, at the time of writing this, not all datasets there carry complete time information. ## References \insertAllCited ## See Also `Fraser2017`, `four_stocks_since2016_01_01` ## Examples ```r data(dataFranses1996) class(dataFranses1996) colnames(dataFranses1996) dim(dataFranses1996) # c(148, 19) plot(dataFranses1996[ , 2:11]) tipi <- dataFranses1996[ , "USTotalIPI"] plot(tipi) ## convert to PeriodicTS and remove NA's at the start and end pctipi <- pcts(tipi) pctipi <- window(pctipi, start = availStart(pctipi), end = availEnd(pctipi)) plot(pctipi) ## convert the whole dataset to class "PeriodicMTS" pcfr <- pcts(dataFranses1996) colnames(pcfr)[2:3] # "USTotalIPI" "CanadaUnemployment" ## subset as "PeriodicMTS" pcfr2to3 <- pcfr[2:3] plot(pcfr2to3) ## "[" "PeriodicMTS" even with length one arg. pcfr2to2 <- pcfr[2] pcfr2to2a <- pcfr["USTotalIPI"] # same ## use "[[" or $ to get "PeriodicTS" pcfr2 <- pcfr[[2]] pcfr2a <- pcfr[["USTotalIPI"]] # same pcfr2b <- pcfr$USTotalIPI # same identical(pcfr2, pcfr2a) # TRUE identical(pcfr2, pcfr2b) # TRUE cycle(pcfr) frequency(pcfr) ```

dataFranses1996 dataset