diebold mariano test on r in r

To conduct the Diebold-Mariano test in R, you'll need to follow these steps:

1. Install and load the "forecast" package:

install.packages("forecast")
library(forecast)
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2. Prepare your forecasts and actual values vectors. Let's assume you have two vectors: forecast1 and forecast2, containing the forecasted values for two different models, and a vector actual containing the actual values.

3. Calculate the forecast errors:

error1 <- actual - forecast1
error2 <- actual - forecast2
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4. Use the Diebold-Mariano test function dm.test() to perform the test:

dm.test(error1, error2, alternative = "two.sided")
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By default, dm.test() assumes that the differences of errors are normally distributed, and it performs a two-sided test. The alternative can also be set to "less" or "greater" if you have a specific hypothesis about which model is better.

5. Interpret the test result. The function will output the test statistic, p-value, and the conclusion of the test. If the p-value is less than the significance level (usually 0.05), you can reject the null hypothesis of no difference in forecasting accuracy.

Remember that the Diebold-Mariano test assumes that the errors are uncorrelated and have constant variance, so it should be used with caution and in combination with other evaluation metrics.

Please note that you will need to adjust the steps according to your specific data and models.