Unrestricted ECM regression
uecm is a generic function used to construct Unrestricted Error
Correction Models (UECM). The function invokes two different
methods. The default method works exactly like
ardl. The other method requires an object of
class 'ardl'. Both methods create the conditional UECM,
which is the UECM of the underlying ARDL.
uecm(...) ## S3 method for class 'ardl' uecm(object, ...) ## Default S3 method: uecm(formula, data, order, start = NULL, end = NULL, ...)
... |
Additional arguments to be passed to the low level regression fitting functions. |
object |
An object of |
formula |
A "formula" describing the linear model. Details for model specification are given under 'Details'. |
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 |
order |
A specification of the order of the underlying ARDL model (e.g.,
for the UECM of an ARDL(1,0,2) model it should be |
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. |
The formula should contain only variables that exist in the data
provided through data plus some additional functions supported by
dynlm (i.e., trend()).
You can also specify fixed variables that are not supposed to be lagged (e.g.
dummies etc.) simply by placing them after |. For example, y ~
x1 + x2 | z1 + z2 where z1 and z2 are the fixed variables and
should not be considered in order. Note that the | notion
should not be confused with the same notion in dynlm where it
introduces instrumental variables.
uecm returns an object of class
c("dynlm", "lm", "uecm"). In addition, attributes 'order', 'data',
'parsed_formula' and 'full_formula' are provided.
The formula of an Unrestricted ECM conditional to an ARDL(p,q1,...,qk) is:
Δ y_{t} = c_{0} + c_{1}t + π_{y}y_{t-1} + ∑_{j=1}^{k}π_{j}x_{j,t-1} + ∑_{i=1}^{p-1}ψ_{y,i}Δ y_{t-i} + ∑_{j=1}^{k}∑_{l=1}^{q_{j}-1} ψ_{j,l}Δ x_{j,t-l} + ∑_{j=1}^{k}ω_{j}Δ x_{j,t} + ε_{t}
Kleanthis Natsiopoulos, klnatsio@gmail.com
data(denmark) ## Estimate the UECM, conditional to it's underlying ARDL(3,1,3,2) ----- # Indirectly ardl_3132 <- ardl(LRM ~ LRY + IBO + IDE, data = denmark, order = c(3,1,3,2)) uecm_3132 <- uecm(ardl_3132) # Directly uecm_3132_ <- uecm(LRM ~ LRY + IBO + IDE, data = denmark, order = c(3,1,3,2)) identical(uecm_3132, uecm_3132_) summary(uecm_3132)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.