MDM.selection: Selects Models with Outstanding Predictive Ability basing on...

In multDM: Multivariate Version of the Diebold-Mariano Test

Selects Models with Outstanding Predictive Ability basing on Multivariate Diebold-Mariano Test.

Description

This function selects models with outstanding predictive ability basing on multivariate Diebold-Mariano test MDM.test.

Usage

MDM.selection(realized,evaluated,q,alpha,statistic="Sc",loss.type="SE")

Arguments

realized	vector of the real values of the modelled time-series
evaluated	matrix of the forecasts, columns correspond to time index, rows correspond to different models
q	numeric indicating a lag length beyond which we are willing to assume that the autocorrelation of loss differentials is essentially zero
alpha	numeric indicating a significance level for multivariate Diebold-Mariano tests
statistic	statistic="S" for the basic version of the test, and statistic="Sc" for the finite-sample correction, if not specified statistic="Sc" is used
loss.type	method to compute the loss function, loss.type="SE" will use squared errors, loss.type="AE" will use absolute errors, loss.type="SPE" will use squred proportional error (useful if errors are heteroskedastic), loss.type="ASE" will use absolute scaled error, if loss.type will be specified as some numeric, then the function of type exp(loss.type*errors)-1-loss.type*errors will be used (useful when it is more costly to underpredict realized than to overpredict), if not specified loss.type="SE" is used

Value

class MDM object, list of

outcomes	matrix with mean losses for the selected models, statistics corresponding to losses differentials and ranking of these statistics
p.value	numeric of p-value from the procedure, i.e., p-value of multivariate Diebold-Mariano test from the last step
alpha	alpha, i.e., the chosen significance level
eliminated	numeric indicating the number of eliminated models

References

Mariano R.S., Preve, D., 2012. Statistical tests for multiple forecast comparison. Journal of Econometrics 169, 123–130.

Examples

data(MDMforecasts)
ts <- MDMforecasts$ts
forecasts <- MDMforecasts$forecasts
MDM.selection(realized=ts,evaluated=forecasts,q=10,alpha=0.1,statistic="S",loss.type="AE")

multDM documentation built on June 9, 2022, 5:06 p.m.