gets (version 0.40)

hpdata: Hoover and Perez (1999) data

Description

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.

Usage

data(hpdata)

Format

Date

a factor that contains the (quarterly) dates of the observations

DCOINC

index of four coincident indicators

GD

GNP price deflator

GGEQ

government purchases of goods and services

GGFEQ

federal purchases of goods and services

GGFR

federal government receipts

GNPQ

GNP

GYDQ

disposable personal income

GPIQ

gross private domestic investment

FMRRA

total member bank reserves

FMBASE

monetary base (feredal reserve bank of St. Louis)

FM1DQ

M1

FM2DQ

M2

FSDJ

Dow Jones stock price

FYAAAC

Moody's AAA corporate bond yield

LHC

labour force (16 years+, civilian)

LHUR

unemployment rate

MU

unfilled orders (manufacturing, all industries)

MO

new orders (manufacturing, all industries)

GCQ

personal consumption expenditure

Details

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).

References

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.

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.

Kevin D. Hoover and Stephen J. Perez (1999): 'Data mining reconsidered: encompassing and the general-to-specific approach to specification search', Econometrics Journal, Vol. 2, pp. 167-191, tools:::Rd_expr_doi("10.1111/1368-423X.00025")

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.

Examples

##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)