BacktestDMA: Backtest measures for Dynamic Model Averaging and comparison...

In eDMA: Dynamic Model Averaging with Grid Search

Description

Backtest measures for Dynamic Model Averaging and comparison with Dynamic Model Selection. This function evaluates the out of sample performance of DMA and compare it with DMS.

Usage

    BacktestDMA(object, iBurnPeriod = NULL)

Arguments

object	an object of the class DMA-class, created using the function DMA.
iBurnPeriod	An integer indicating the length of the burn-in period. By default iBurnPeriod = NULL. If iBurnPeriod = NULL then one third of the total sample is used as the burn-in in period and a warning is printed.

Details

The function returns a matrix with Mean Squared Error (MSE), Mean Absolute Error (MAD) and Predictive Likelihood for DMA and DMS using the predictions during the out-of-sample period.

Value

An object of the class matrix.

Author(s)

Leopoldo Catania & Nima Nonejad

Examples

## Not run: 

library(eDMA)

## load data
data("USData")

## do DMA, keep the first three predictors fixed and the intercept
Fit <- DMA(GDPDEF ~ Lag(GDPDEF, 1) + Lag(GDPDEF, 2) + Lag(GDPDEF, 3) +
            Lag(ROUTP, 1) + Lag(UNEMP, 1), data = USData, vDelta = c(0.9, 0.95, 0.99),
            vKeep = c(1, 2, 3))

BacktestDMA(Fit, iBurnPeriod = 32)


## End(Not run)

eDMA documentation built on May 29, 2017, 5:37 p.m.