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Linear regression forecasting in the presence of ar(1) disturbances

✍ Scribed by Abdul Latif; Maxwell L. King


Publisher
John Wiley and Sons
Year
1993
Tongue
English
Weight
679 KB
Volume
12
Category
Article
ISSN
0277-6693

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✦ Synopsis


This paper is concerned with time-series forecasting based on the linear regression model in the presence of AR(1) disturbances. The standard approach is to estimate the AR(1) parameter, p, and then construct forecasts assuming the estimated value is the true value. We introduce a new approach which can be viewed as a weighted average of predictions assuming different values of p. The weights are proportional to the marginal likelihood of p. A Monte Carlo experiment was conducted to compare the new method with five more conventional predictors. Its results suggest that the new approach has a distinct edge over existing procedures.


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