Rolling regression forecast , DM test, CW test

Question

I have a linear model with the exchange rate as a dependent variable and 7 others independent variables(e.g. inflation, interest rate etc.). I have quarterly data from 1993Q1-2011Q4. I would like to create a rolling window regression (with the model above) with window size 60(from 1993Q1-2007Q4) and use the estimated regression to forecast the rest sample. Also, I would like to compare this model with the Random Walk model(exchange rate follows a R.W.). In the end, I would like to perform the dm.test and clarkwest test(does not run). Is my code right?

X = embed(data)
X = as.data.frame(X)

install.packages("foreach")
library(foreach)
w_size=60
n_windows = nrow(X) - 60 #until 2007Q4
forecasts = foreach(i=1:n_windows, .combine = rbind) %do%{
    # = Select data for the window (in and out-of-sample) = #
  X_in = X[i:(w_size + i - 1), ] # = change to X[1:(w_size + i - 1), ] for expanding window
  X_out = X[w_size + i, ]

  # = Regression Model = #
  m1 = lm(V1 ~ V2+V3+V4+V5+V6+V7+V8, data = X_in)
  f1 = predict(m1, X_out)

  # = Random Walk = #
  f2 = tail(X_in$V1, 1)

  return(c(f1, f2))
}
 e1 = tail(X[ ,"V1"], nrow(forecasts)) - forecasts[ ,1]
 e2 = tail(X[ ,"V1"], nrow(forecasts)) - forecasts[ ,2]
library(tseries)     
library(forecast)
dm.test(e1,e2, "l") #p-value is more than 5% for all the cases( two.sided, greater, less)
clarkwest(e1,e2)

Answer 1

It seems like the clarkwest() function is not supported anymore. I recently wrote my own function: CW Note that I used normal standard errors and not Newey-West corrected.

To investigate your loop you could try:

i=1
X_in = X[i:(w_size + i - 1), ] # = change to X[1:(w_size + i - 1), ] for expanding window
X_out = X[w_size + i, ]

# = Regression Model = #
m1 = lm(V1 ~ V2+V3+V4+V5+V6+V7+V8, data = X_in)
f1 = predict(m1, X_out)

# = Random Walk = #
f2 = tail(X_in$V1, 1)

Here you can see the composition the loop creates when i=1