Understanding Time-Series Regression Estimators

A large number of methods have been developed for estimating time-series regression parameters. Students and practitioners have a difficult time understanding what these various methods are, let alone picking the most appropriate one for their application. This article explains how these methods are...

Full description

Saved in:
Bibliographic Details
Published in:The American statistician 1999-11, Vol.53 (4), p.342-348
Main Authors: Choudhury, Askar H., Hubata, Robert, St. Louis, Robert D.
Format: Article
Language:English
Subjects:
Applied mathematics
Approximate and exact estimators
Autoregressive and moving average error models
Autoregressive moving average
Cholesky decomposition
Computational convenience
Econometrics
Economic models
Estimates
Estimation methods
Estimators
Exact sciences and technology
Generalized least squares and maximum likelihood estimators
Inference from stochastic processes
time series analysis
Least squares
Linear and nonlinear optimization methods
Mathematics
Maximum likelihood estimation
Parameter estimation
Parametric models
Probability and statistics
Regression analysis
Sciences and techniques of general use
Simulation
Software
Statistical methods
Statistics
Stochastic models
Time series
Time series models
Transformations to obtain uncorrelated errors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A large number of methods have been developed for estimating time-series regression parameters. Students and practitioners have a difficult time understanding what these various methods are, let alone picking the most appropriate one for their application. This article explains how these methods are related. A chronology for the development of the various methods is presented, followed by a logical characterization of the methods. An examination of current computational techniques and computing power leads to the conclusion that exact maximum likelihood estimators should be used in almost all cases where regression models have autoregressive, moving average, or mixed autoregressive-moving average error structures.
ISSN:0003-1305
1537-2731
DOI:10.1080/00031305.1999.10474487