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Linear regression forecasting in the presence of ar(1) disturbances
Author(s) -
Latif Abdul,
King Maxwell L.
Publication year - 1993
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980120605
Subject(s) - linear regression , monte carlo method , regression , series (stratigraphy) , econometrics , statistics , value (mathematics) , mathematics , proper linear model , computer science , bayesian multivariate linear regression , paleontology , biology
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, ρ, 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 ρ. The weights are proportional to the marginal likelihood of ρ. 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.