## Abstract A linear regression model with random walk coefficients is extended to allow for linear restrictions between the coefficients to be satisfied at each point in time. Estimation in this model is shown to be no more involved than estimation in the standard model. It is also demonstrated ho
Linear regression forecasting in the presence of ar(1) disturbances
β 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
No coin nor oath required. For personal study only.
β¦ 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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