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Answer: Root mean squared error
## Explanation When comparing out-of-sample forecasting performance, **Root Mean Squared Error (RMSE)** is the most appropriate metric because: - **RMSE** measures the average magnitude of forecasting errors, with larger errors being penalized more heavily due to squaring - It directly assesses predictive accuracy on unseen data - Lower RMSE values indicate better forecasting performance **Why other options are incorrect:** - **$R^2$**: Measures in-sample goodness of fit, not out-of-sample predictive accuracy - **Durbin-Watson statistic**: Tests for autocorrelation in residuals, not forecasting performance For time series forecasting model comparison, RMSE is the standard metric for evaluating out-of-sample predictive accuracy.
Author: LeetQuiz Editorial Team
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An analyst is comparing the out-of-sample forecasting performance of an AR(1) model and an AR(2) model for monthly inflation rates. Which of the following metrics is the most appropriate for making this comparison?
A
B
Durbin–Watson statistic
C
Root mean squared error
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