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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-12-8674 |
| Date | 2010 December 1900 |
| Creators | Kudoyan, Olga |
| Contributors | Bryant, Henry L., Richardson, James W. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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