auto_ardl {ARDL}  R Documentation 
It searches for the best ARDL order specification, according to the selected criterion, taking into account the constraints provided.
auto_ardl(
formula,
data,
max_order,
fixed_order = 1,
starting_order = NULL,
selection = "AIC",
selection_minmax = c("min", "max"),
grid = FALSE,
search_type = c("horizontal", "vertical"),
start = NULL,
end = NULL,
...
)
formula 
A "formula" describing the linear model. Details for model
specification are given under 'Details' in the help file of the

data 
A time series object (e.g., "ts", "zoo" or "zooreg") or a data
frame containing the variables in the model. In the case of a data frame,
it is coerced into a 
max_order 
It sets the maximum order for each variable where the search
is taking place. A numeric vector of the same length as the total number of
variables (excluding the fixed ones, see 'Details' in the help file of the

fixed_order 
It allows setting a fixed order for some variables. The
algorithm will not search for any other order than this. A numeric vector
of the same length as the total number of variables (excluding the fixed
ones). It should contain positive integers or 0 to set as a constraint. A
1 should be provided for any variable that should not be constrained.

starting_order 
Specifies the order for each variable from which each
search will start. It is a numeric vector of the same length as the total
number of variables (excluding the fixed ones). It should contain positive
integers or 0 or only one integer could be provided if the starting order
for all variables is the same. Default is set to NULL. If unspecified
( 
selection 
A character string specifying the selection criterion
according to which the candidate models will be ranked. Default is

selection_minmax 
A character string that indicates whether the
criterion in 
grid 
If 
search_type 
A character string describing the search type. If
"horizontal" (default), the searching algorithm increases or decreases by 1
the order of each variable in each iteration. When the order of the last
variable has been accessed, it begins again from the first variable until
it converges. If "vertical", the searching algorithm increases or decreases
by 1 the order of a variable until it converges. Then it continues the same
for the next variable. The two options result to very similar top orders.
The default ("horizontal"), sometimes is a little more accurate, but the
"vertical" is almost 2 times faster. Not applicable if 
start 
Start of the time period which should be used for fitting the model. 
end 
End of the time period which should be used for fitting the model. 
... 
Additional arguments to be passed to the low level regression fitting functions. 
auto_ardl
returns a list which contains:
best_model 
An object of class 
best_order 
A numeric vector with the order of the best model selected 
top_orders 
A data.frame with the orders of the top 20 models 
The algorithm performs the optimization process
starting from multiple starting points concerning the autoregressive order
p
. The searching algorithm will perform a complete search, each time
starting from a different starting order. These orders are presented in the
tables below, for grid = FALSE
and different values of
starting_order
.
starting_order = NULL
:
ARDL(p)  >  p  q1  q2  ...  qk 
ARDL(1)  >  1  1  1  ...  1 
ARDL(2)  >  2  2  2  ...  2 
:  >  :  :  :  :  : 
ARDL(P)  >  P  P  P  ...  P 
starting_order = c(3, 0, 1, 2)
:
p  q1  q2  q3 
3  0  1  2 
4  0  1  2 
:  :  :  : 
P  0  1  2 
Kleanthis Natsiopoulos, klnatsio@gmail.com
data(denmark)
## Find the best ARDL order 
# Up to 5 for the autoregressive order (p) and 4 for the rest (q1, q2, q3)
# Using the defaults search_type = "horizontal", grid = FALSE and selection = "AIC"
# ("Not run" indications only for testing purposes)
## Not run:
model1 < auto_ardl(LRM ~ LRY + IBO + IDE, data = denmark,
max_order = c(5,4,4,4))
model1$top_orders
## Same, with search_type = "vertical" 
model1_h < auto_ardl(LRM ~ LRY + IBO + IDE, data = denmark,
max_order = c(5,4,4,4), search_type = "vertical")
model1_h$top_orders
## Find the global optimum ARDL order 
# It may take more than 10 seconds
model_grid < auto_ardl(LRM ~ LRY + IBO + IDE, data = denmark,
max_order = c(5,4,4,4), grid = TRUE)
## Different selection criteria 
# Using BIC as selection criterion instead of AIC
model1_b < auto_ardl(LRM ~ LRY + IBO + IDE, data = denmark,
max_order = c(5,4,4,4), selection = "BIC")
model1_b$top_orders
# Using other criteria like adjusted R squared (the bigger the better)
adjr2 < function(x) { summary(x)$adj.r.squared }
model1_adjr2 < auto_ardl(LRM ~ LRY + IBO + IDE, data = denmark,
max_order = c(5,4,4,4), selection = "adjr2",
selection_minmax = "max")
model1_adjr2$top_orders
# Using functions from other packages as selection criteria
if (requireNamespace("qpcR", quietly = TRUE)) {
library(qpcR)
model1_aicc < auto_ardl(LRM ~ LRY + IBO + IDE, data = denmark,
max_order = c(5,4,4,4), selection = "AICc")
model1_aicc$top_orders
adjr2 < function(x){ Rsq.ad(x) }
model1_adjr2 < auto_ardl(LRM ~ LRY + IBO + IDE, data = denmark,
max_order = c(5,4,4,4), selection = "adjr2",
selection_minmax = "max")
model1_adjr2$top_orders
## DIfferent starting order 
# The searching algorithm will start from the following starting orders:
# p q1 q2 q3
# 1 1 3 2
# 2 1 3 2
# 3 1 3 2
# 4 1 3 2
# 5 1 3 2
model1_so < auto_ardl(LRM ~ LRY + IBO + IDE, data = denmark,
max_order = c(5,4,4,4), starting_order = c(1,1,3,2))
# Starting from p=3 (don't search for p=1 and p=2)
# Starting orders:
# p q1 q2 q3
# 3 1 3 2
# 4 1 3 2
# 5 1 3 2
model1_so_3 < auto_ardl(LRM ~ LRY + IBO + IDE, data = denmark,
max_order = c(5,4,4,4), starting_order = c(3,1,3,2))
# If starting_order = NULL, the starting orders for each iteration will be:
# p q1 q2 q3
# 1 1 1 1
# 2 2 2 2
# 3 3 3 3
# 4 4 4 4
# 5 5 5 5
}
## Add constraints 
# Restrict only the order of IBO to be 2
model1_ibo2 < auto_ardl(LRM ~ LRY + IBO + IDE, data = denmark,
max_order = c(5,4,4,4), fixed_order = c(1,1,2,1))
model1_ibo2$top_orders
# Restrict the order of LRM to be 3 and the order of IBO to be 2
model1_lrm3_ibo2 < auto_ardl(LRM ~ LRY + IBO + IDE, data = denmark,
max_order = c(5,4,4,4), fixed_order = c(3,1,2,1))
model1_lrm3_ibo2$top_orders
## Set the starting date for the regression (data starts at "1974 Q1") 
# Set regression starting date to "1976 Q1"
model1_76q1 < auto_ardl(LRM ~ LRY + IBO + IDE, data = denmark,
max_order = c(5,4,4,4), start = "1976 Q1")
start(model1_76q1$best_model)
## End(Not run)