Lagged predictor forecasting and Scenario-based forecasting

Hi,
I've run into some issues and wanted to clarify two points.

• First, when using a dynamic regression model to create forecasts for the next 3 periods, are future values required even when all the predictors are lagged (see below example)?

  example = ARIMA(DV ~ trend() + season()
  + lag(Var1,6)
  + lag(Var2,6)
  + lag(Var3,3))

• Second, I have attempted to use scenario-based forecasting (as outlined in Chp 7.6) and wanted to seek clarification on the code (see example below taken from the text. The text describes the numbers bolded below as percentage increase, but how does this translate in the code? When I run the code, it simply creates a dataframe with 1 and 0.5?

  future_scenarios <- scenarios(
    Increase = new_data(us_change, 4) %>%
      mutate(Income=**1**, Savings=**0.5**, Unemployment=**0**),
    Decrease = new_data(us_change, 4) %>%
     mutate(Income=**-1**, Savings=**-0.5**, Unemployment=**0**),
   names_to = "Scenario")
  

Apologies if I have left some information out and thank you for any help.

Start with the linked FPP3 Chapter 7.6 and the two forecasting examples in the issue. Review how lagged predictors and scenarios are described, then clarify the future-value requirement and what the scenario values represent; the issue provides no repository files or tests to run.