Data used by Hoover and Perez (1999) in their evaluation of General-to-Specific (GETS) modelling. A detailed description of the data is found in their Table 1 (page 172). The data are quarterly, comprise 20 variables (the first variable is the quarterly index) and runs from 1959:1 to 1995:1. This corresponds to 145 observations. The original source of the data is Citibank.
a factor that contains the (quarterly) dates of the observations
index of four coincident indicators
GNP price deflator
government purchases of goods and services
federal purchases of goods and services
federal government receipts
disposable personal income
gross private domestic investment
total member bank reserves
monetary base (feredal reserve bank of St. Louis)
Dow Jones stock price
Moody's AAA corporate bond yield
labour force (16 years+, civilian)
unfilled orders (manufacturing, all industries)
new orders (manufacturing, all industries)
personal consumption expenditure
The data have been used for comparison and illustration of GETS model selection in several studies of the GETS methodology, including Hendry and Krolzig (1999, 2005), Doornik (2009) and Sucarrat and Escribano (2012).
Retrieved 14 October 2014 from: http://www.csus.edu/indiv/p/perezs/Data/data.htm
David F. Hendry and Hans-Martin Krolzig (1999): 'Improving on 'Data mining reconsidered' by K.D. Hoover and S.J Perez', Econometrics Journal, Vol. 2, pp. 202-219.
David F. Hendry and Hans-Martin Krolzig (2005): 'The properties of automatic Gets modelling', Economic Journal 115, C32-C61.
Jurgen Doornik (2009): 'Autometrics', in Jennifer L. Castle and Neil Shephard (eds), 'The Methodology and Practice of Econometrics: A Festschrift in Honour of David F. Hendry', Oxford University Press, Oxford, pp. 88-121.
Pretis, Felix, Reade, James and Sucarrat, Genaro (2018): 'Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks'. Journal of Statistical Software 86, Number 3, pp. 1-44.
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##load Hoover and Perez (1999) data: data(hpdata) ##make quarterly data-matrix of zoo type: newhpdata <- zooreg(hpdata[,-1], start=c(1959,1), frequency=4) ##plot data: plot(newhpdata) ##transform data to log-differences in percent: dloghpdata <- diff(log(newhpdata))*100 ##plot log-differenced data: plot(dloghpdata)
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