dLagM (version 1.0.2)

MASE: Compute mean absolute scaled error (MASE) for distributed lag models

Description

A function that computes mean absolute scaled error for fitted DLMs.

Usage

MASE(model, ...)

Arguments

model

Model object fitted for time series data.

...

Optionally, more fitted models.

Value

MASE

Mean absolute scaled error (MASE) for the observed and fitted series sent into the function.

Details

Let \(e_{t} = Y_{t}-\hat{Y}_{t}\) be the one-step-ahead forecast error. Then, a scaled error is defined as

$$ q_{t}=\frac{e_{t}}{\frac{1}{n-1}\sum_{i=2}^{n}|Y_{i}-Y_{i-1}|}, $$ which is independent of the scale of the data. Mean absolute scaled error is defined as

$$ MASE = mean(|q_{t}|) $$ (Hyndman and Koehler, 2006).

Fitted models would be finite, polynomial, Koyck, ARDL DLMs, or linear model fitted with lm() function. This function also computes MASE values of multiple models when fed at the same time.

References

Hyndman, R.J. and Koehler, A.B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22, 679-688.

Examples

# NOT RUN {
data(warming)
# Fit a bunch of polynomial DLMs
model.poly1 = polyDlm(x = warming$NoMotorVehicles , y = warming$Warming , 
                     q = 2 , k = 2 , show.beta = TRUE ,  show.summary = FALSE)
model.poly2 = polyDlm(x = warming$NoMotorVehicles , y = warming$Warming , 
                      q = 3 , k = 2 , show.beta = TRUE ,  show.summary = FALSE)
model.poly3 = polyDlm(x = warming$NoMotorVehicles , y = warming$Warming , 
                      q = 4 , k = 2 , show.beta = TRUE ,  show.summary = FALSE)
MASE(model.poly1, model.poly2, model.poly3)

# Fit a bunch of finite DLMs
model.dlm1 = dlm(x = warming$NoMotorVehicles , y = warming$Warming, q =2)
model.dlm2 = dlm(x = warming$NoMotorVehicles , y = warming$Warming, q =3)
model.dlm3 = dlm(x = warming$NoMotorVehicles , y = warming$Warming, q =4)
MASE(model.dlm1, model.dlm2, model.dlm3)

# Fit a linear model
model.lm = lm(Warming ~ NoMotorVehicles , data = warming)
MASE(model.lm)

# Fit a Koyck model
model.koyck = koyckDlm(x = warming$NoMotorVehicles , y = warming$Warming )
MASE(model.koyck)

# Fit a bunch of ARDLs
model.ardl1 = ardlDlm(x = warming$NoMotorVehicles , y = warming$Warming, p=1, q=2)
model.ardl2 = ardlDlm(x = warming$NoMotorVehicles , y = warming$Warming, p=2, q=2)
model.ardl3 = ardlDlm(x = warming$NoMotorVehicles , y = warming$Warming, p=3, q=2)
MASE(model.ardl1 , model.ardl2 , model.ardl3)

# Find MASEs of different model objects
MASE(model.ardl1 , model.dlm1 , model.poly1, model.lm)
# }