Kudoyan, Olga (2010-12). The Incremental Benefits of the Nearest Neighbor Forecast of U.S. Energy Commodity Prices. Master's Thesis. Thesis uri icon

abstract

  • This thesis compares the simple Autoregressive (AR) model against the k- Nearest Neighbor (k-NN) model to make a point forecast of five energy commodity prices. Those commodities are natural gas, heating oil, gasoline, ethanol, and crude oil. The data for the commodities are monthly and, for each commodity, two-thirds of the data are used for an in-sample forecast, and the remaining one-third of the data are used to perform an out-of-sample forecast. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used to compare the two forecasts. The results showed that one method is superior by one measure but inferior by another. Although the differences of the two models are minimal, it is up to a decision maker as to which model to choose. The Diebold-Mariano (DM) test was performed to test the relative accuracy of the models. For all five commodities, the results failed to reject the null hypothesis indicating that both models are equally accurate.
  • This thesis compares the simple Autoregressive (AR) model against the k-
    Nearest Neighbor (k-NN) model to make a point forecast of five energy commodity
    prices. Those commodities are natural gas, heating oil, gasoline, ethanol, and crude oil.
    The data for the commodities are monthly and, for each commodity, two-thirds of the
    data are used for an in-sample forecast, and the remaining one-third of the data are used
    to perform an out-of-sample forecast. Mean Absolute Error (MAE) and Root Mean
    Squared Error (RMSE) are used to compare the two forecasts. The results showed that
    one method is superior by one measure but inferior by another. Although the differences
    of the two models are minimal, it is up to a decision maker as to which model to choose.
    The Diebold-Mariano (DM) test was performed to test the relative accuracy of
    the models. For all five commodities, the results failed to reject the null hypothesis
    indicating that both models are equally accurate.

publication date

  • December 2010