Dynamic models using TSLM (ARDL)

[defra-ma]

I am trying to estimate an ARDL(p,q). I have used the ARDL package for estimating such models in the past, but would like to use fable's scenario forecasting and bootstrapping tools. ARDL's are just linear models so, lacking a specific ARDL model I should be able to estimate using TSLM.

If I use TSLM I cannot use the order special from AR. While I can estimate a model using dplyr::lag, TSLM doesn't know this is a lag of the dependent variable and so cannot generate a dynamic forecast.

I can estimate any ARDL(p, 0) using AR, but that is only useful when the lag q on the exogenous regressor(s) is zero. The order() special cannot (as far as I know) create lags of an exogenous regressor.

A related issue occurs using scenario, where scenarios featuring lagged regressors created using lag are not recognised.

Is there a way around this without diving into fabletools and creating a new model class (and presumably, fable.ARDL)?

== Edit (28/3):

If one uses I() to wrap any lagged values, e.g. TSLM(y ~ I(lag(y)) + x + I(lag(x)) correctly estimates an ARDL(1,1). The forecast function can then correctly generate the lagged values of x, but then only generates static forecasts as it doesn't know that I(lag(y)) is a lagged dependent variable. However, it seems we can use the order() special from AR which allows us to get a dynamic forecast conditioned on the path(s) of the exogenous variable(s):

library(fable)
library(tsibble)
library(tidyverse)

set.seed(123)
df = tibble(
  time = yearquarter(seq.Date(from = yq("1960-01"), to = yq("1986-04"), by = "quarter")),
  y = as.double(UKgas),
  x = cumprod(c(1, 1 + rnorm(length(y)-1, mean = 0.03, sd = 0.02)))
) %>% 
  as_tsibble(index = time)

train = filter(df, as.Date(time) <= yq("1980-01"))
test = filter(df, as.Date(time) > yq("1980-01")) %>% 
  select(-y)

fit <- train %>% 
  model(
    ar = AR(y ~ order(1) + xreg(I(x)) + xreg(I(lag(x))))
  )

forecast(fit, test) %>% 
  autoplot(train)