Optimize lag order for the autoregressive model

nmfkc.ar.degree.cv selects the optimal lag order for an autoregressive model by applying cross-validation over candidate degrees.

This function accepts both standard matrices (Variables x Time) and ts objects (Time x Variables). ts objects are automatically transposed internally.

Usage

nmfkc.ar.degree.cv(Y, Q = 1, degree = 1:2, intercept = TRUE, plot = TRUE, ...)

Arguments

Y

Observation matrix \(Y(P,N)\) or a ts object.

Q

Rank of the basis matrix. Must satisfy \(Q \le \min(P,N)\).

degree

A vector of candidate lag orders to be evaluated.

intercept

Logical. If TRUE (default), an intercept is added to the covariate matrix.

plot

Logical. If TRUE (default), a plot of the objective function values is drawn.

...

Additional arguments passed to nmfkc.cv.

Value

A list with components:

degree

The lag order that minimizes the cross-validation objective function.

degree.max

Maximum recommended lag order, computed as \(10 \log_{10}(N)\) following the ar function in the stats package.

objfunc

Objective function values for each candidate lag order.

See also

nmfkc.ar, nmfkc.cv

Examples

# install.packages("remotes")
# remotes::install_github("ksatohds/nmfkc")
# Example using ts object directly
d <- AirPassengers

# Selection of degree (using ts object)
# Note: Y is automatically transposed if it is a ts object
nmfkc.ar.degree.cv(Y=d, Q=1, degree=11:14)
#> degree=11...
#> 0sec
#> degree=12...
#> 0.1sec
#> degree=13...
#> 0.1sec
#> degree=14...
#> 0.1sec

#> $degree
#> [1] 12
#> 
#> $degree.max
#> [1] 21
#> 
#> $objfunc
#>       11       12       13       14 
#> 507.7053 271.4061 276.0141 278.1065 
#>