# dm.test

0th

Percentile

##### Diebold-Mariano test for predictive accuracy

The Diebold-Mariano test compares the forecast accuracy of two forecast methods. The null hypothesis is that they have the same forecast accuracy.

Keywords
ts, htest
##### Usage
dm.test(e1, e2, alternative = c("two.sided", "less", "greater"), h = 1, power = 2)
##### Arguments
e1
Forecast errors from method 1.
e2
Forecast errors from method 2.
alternative
a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.
h
The forecast horizon used in calculating e1 and e2.
power
The power used in the loss function. Usually 1 or 2.
##### Value

• A list with class "htest" containing the following components:
• statisticthe value of the DM-statistic.
• parameterthe forecast horizon and loss function power used in the test.
• alternativea character string describing the alternative hypothesis.
• p.valuethe p-value for the test.
• methoda character string with the value "Diebold-Mariano Test".
• data.namea character vector giving the names of the two error series.

##### References

Diebold, F.X. and Mariano, R.S. (1995) Comparing predictive accuracy. Journal of Business and Economic Statistics, 13, 253-263.

• dm.test
##### Examples
# Test on in-sample one-step forecasts
f1 <- ets(WWWusage)
f2 <- auto.arima(WWWusage)
accuracy(f1)
accuracy(f2)
dm.test(residuals(f1),residuals(f2),h=1)

# Test on out-of-sample one-step forecasts
f1 <- ets(WWWusage[1:80])
f2 <- auto.arima(WWWusage[1:80])
f1.out <- ets(WWWusage[81:100],model=f1)
f2.out <- Arima(WWWusage[81:100],model=f2)
accuracy(f1.out)
accuracy(f2.out)
dm.test(residuals(f1.out),residuals(f2.out),h=1)
Documentation reproduced from package forecast, version 2.01, License: GPL (>= 2)

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