R predict expecting variable not in lm object

I've built a method to create an error correction model (ECM) that is an average of multiple ECMs. To do this, I'm leveraging the lm() function in R to create multiple lm objects that represent ECMs. I'm averaging the coefficients of each object to obtain the final model. The way the lm objects represent ECMs is that I transform the data to the format necessary for an ECM before running lm() on the data.

I also use backwards selection using AIC to eliminate variables I don't need. The process seems to work fine in creating an ECM. However, when I create a data frame with column names that match the coefficients in my model, I get an error saying a necessary variable is missing from the data. However, in the final model this variable was not included, so it shouldn't be necessary to predict on. So why is predict() looking for that variable? What am I doing wrong?

#Load data
library(ecm)
data(Wilshire)
trn <- Wilshire[Wilshire$date<='2015-12-01',]
y <- trn$Wilshire5000
xeq <- xtr <- trn[c('CorpProfits', 'FedFundsRate', 'UnempRate')]

#Function to split data into k partitions and build k models, each on a (k-1)/k subset of the data
avelm <- function(formula, data, k = 5, seed = 5, ...) {
  lmall <- lm(formula, data, ...)
  modellist <- 1:k
  set.seed(seed)
  models <- lapply(modellist, function(i) {
    tstIdx <- sample(nrow(data), 1/k * nrow(data))
    trn <- data[-tstIdx, ]
    lm(as.formula(formula), data = trn)
  })
  lmnames <- names(lmall$coefficients)
  lmall$coefficients <- rowMeans(as.data.frame(sapply(models, function(m) coef(m))))
  names(lmall$coefficients) <- lmnames
  lmall$fitted.values <- predict(lmall, data)
  target <- trimws(gsub("~.*$", "", formula))
  lmall$residuals <- data[, target] - lmall$fitted.values
  return(lmall)
}

#Function to create an ECM using backwards selection based on AIC (leveraged avelm function above)
aveecmback <- function (y, xeq, xtr, k = 5, seed = 5, ...) {
  xeqnames <- names(xeq)
  xeqnames <- paste0(xeqnames, "Lag1")
  xeq <- as.data.frame(xeq)
  xeq <- rbind(rep(NA, ncol(xeq)), xeq[1:(nrow(xeq) - 1), ])

  xtrnames <- names(xtr)
  xtrnames <- paste0("delta", xtrnames)
  xtr <- as.data.frame(xtr)
  xtr <- data.frame(apply(xtr, 2, diff, 1))
  yLag1 <- y[1:(length(y) - 1)]
  x <- cbind(xtr, xeq[complete.cases(xeq), ])
  x <- cbind(x, yLag1)
  names(x) <- c(xtrnames, xeqnames, "yLag1")
  x$dy <- diff(y, 1)
  formula <- "dy ~ ."

  model <- avelm(formula, data = x, k = k, seed = seed, ...)
  fullAIC <- partialAIC <- AIC(model)
  while (partialAIC <= fullAIC) {
    todrop <- rownames(drop1(model))[-grep("none|yLag1", rownames(drop1(model)))][which.min(drop1(model)$AIC[-grep("none|yLag1", rownames(drop1(model)))])]
    formula <- paste0(formula, " - ", todrop)
    model <- avelm(formula, data = x, seed = seed, ...)
    partialAIC <- AIC(model)
    if (partialAIC < fullAIC & length(rownames(drop1(model))) > 2) {
      fullAIC <- partialAIC
    }
  }

  return(model)
}

finalmodel <- aveecmback(y, xeq, xtr)
print(finalmodel)

Call:
lm(formula = formula, data = data)

Coefficients:
     (Intercept)  deltaCorpProfits    deltaUnempRate   CorpProfitsLag1             yLag1  
       -0.177771          0.012733         -1.204489          0.002046         -0.024294  

#Create data frame to predict on
set.seed(2)
df <- data.frame(deltaCorpProfits=rnorm(5), deltaUnempRate=rnorm(5), CorpProfitsLag1=rnorm(5), yLag1=rnorm(5))

predict(finalmodel, df)
Error in eval(predvars, data, env) : object 'deltaFedFundsRate' not found

I figured it out. The problem is in the portion of the aveecmback() function where I modify formula inside the while loop. If instead I modify x to drop the variable, the problem goes away. That's because something like this still requires disp in the dataframe, even though it's been removed in the formula:

data(mtcars)
model <- lm(mpg~.-disp, mtcars)
predict(model, mtcars[-which(names(mtcars) %in% 'disp')])
Error in eval(predvars, data, env) : object 'disp' not found

However, something like this will allow predict() to work on a dataframe without disp:

data(mtcars)
model <- lm(mpg~., mtcars[-which(names(mtcars) %in% 'disp')])
predict(model, mtcars[-which(names(mtcars) %in% 'disp')])
          Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive   Hornet Sportabout 
           22.37587            22.07853            26.58631            20.82285            17.26052 
            Valiant          Duster 360           Merc 240D            Merc 230            Merc 280 
           20.46572            14.04956            22.38273            24.20323            18.97756 
          Merc 280C          Merc 450SE          Merc 450SL         Merc 450SLC  Cadillac Fleetwood 
           19.37670            15.10244            16.12864            16.26339            11.31787 
Lincoln Continental   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
           10.68985            10.65062            28.03687            29.29545            29.42472 
      Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28    Pontiac Firebird 
           23.72382            16.91215            17.78366            13.53713            16.15156 
          Fiat X1-9       Porsche 914-2        Lotus Europa      Ford Pantera L        Ferrari Dino 
           28.35383            26.31886            27.36155            18.86561            19.75073 
      Maserati Bora          Volvo 142E 
           13.86302            24.78865