Automated multi-path General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting structural breaks in the mean. The mean can be specified as an autoregressive model with covariates (an 'AR-X' model), and the variance can be specified as a log-variance model with covariates (a 'log-ARCH-X' model).
The four main functions of the package are
isat. The first function,
arx, estimates an AR-X model with (optionally) log-ARCH-X errors. The second function,
getsm, undertakes GETS model selection of the mean specification of an
arx object. The third function,
getsv, undertakes GETS model selection of the log-variance specification of an
arx object. The fourth function,
isat, undertakes GETS model selection of an indicator saturated mean specification.
The package also provides auxiliary functions used by the main functions, in addition to extraction functions (mainly S3 methods).
The code originated in relation with G. Sucarrat and A. Escribano (2012): 'Automated Financial Model Selection: General-to-Specific Modelling of the Mean and Volatility
Specifications', Oxford Bulletin of Economics and Statistics 74, Issue 5 (October), pp. 716-735. Subsequently, Felix Pretis and James Reade joined for the development of the
isat code and related functions
G. Sucarrat and A. Escribano (2012): 'Automated Financial Model Selection: General-to-Specific Modelling of the Mean and Volatility Specifications', Oxford Bulletin of Economics and Statistics 74, Issue 5 (October), pp. 716-735
Carlos Santos, Hendry, David, F. and Johansen, Soren (2007): 'Automatic selection of indicators in a fully saturated regression'. Computational Statistics, vol 23:1, pp.317-335
Jurgen, A. Doornik, Hendry, David F., and Pretis, Felix (2013): 'Step Indicator Saturation', Oxford Economics Discussion Paper, 658.
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##Simulate from an AR(1): set.seed(123) y <- arima.sim(list(ar=0.4), 60) ##Estimate an AR(2) with intercept as mean specification ##and a log-ARCH(4) as log-volatility specification: myModel <- arx(y, mc=TRUE, ar=1:2, arch=1:4) ##GETS modelling of the mean of myModel: simpleMean <- getsm(myModel) ##GETS modelling of the log-variance of myModel: simpleVar <- getsv(myModel) ##results: print(simpleMean) print(simpleVar) ##step indicator saturation of an iid normal series: set.seed(123) y <- rnorm(30) isat(y)