๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Regression forecasts when disturbances are autocorrelated

โœ Scribed by Terry E. Dielman

Book ID
102843210
Publisher
John Wiley and Sons
Year
1985
Tongue
English
Weight
538 KB
Volume
4
Category
Article
ISSN
0277-6693

No coin nor oath required. For personal study only.

โœฆ Synopsis

A Monte Carlo simulation is used to study the quality of forecasts obtained from regression models with various degrees of autocorrelation present in the disturbances. The methods used to estimate the model parameters include least squares, full maximum likelihood, Prais-Winsten, Cochrane-Orcutt and Bayesian estimation. Results indicate that the Cochrane-Orcutt method should be avoided. The full maximum likelihood, Prais-Winsten and Bayesian methods are relatively more efficient than least squares when the degree of autocorrelation is high (greater than or equal to 0.5) and show little efficiency loss when the degree is low. These results hold for both trended and untrended independent variables.

๐Ÿ“œ SIMILAR VOLUMES

Linear regression forecasting in the pre
Linear regression forecasting in the presence of ar(1) disturbances
โœ Abdul Latif; Maxwell L. King ๐Ÿ“‚ Article ๐Ÿ“… 1993 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 679 KB

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