Abstract
Abstract We point out that autocorrelated error terms require modification of the usual methods of estimation and prediction; and we present evidence showing that the error terms involved in most current formulations of economic relations are highly positively autocorrelated. In doing this we demonstrate that when estimates of autoregressive properties of error terms are based on calculated residuals there is a large bias towards randomness. We demonstrate how much efficiency may be lost by current methods of estimation and prediction; and we give a tentative method of procedure for regaining the lost efficiency.
Keywords
AutocorrelationAutoregressive modelStatisticsRandomnessEconometricsMathematicsRegressionEstimationLeast-squares function approximationRegression analysisGeneralized least squaresEconomics
Related Publications
Methods to account for spatial autocorrelation in the analysis of species distributional data: a review
Species distributional or trait data based on range map (extent‐of‐occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exh...
Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressi...
Applied Linear Regression
Preface.1 Scatterplots and Regression.1.1 Scatterplots.1.2 Mean Functions.1.3 Variance Functions.1.4 Summary Graph.1.5 Tools for Looking at Scatterplots.1.5.1 Size.1.5.2 Transfo...
Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models
In a previous article we showed that ordinary least squares with panel corrected standard errors is superior to the Parks generalized least squares approach to the estimation of...
Subset Selection in Regression
OBJECTIVES Prediction, Explanation, Elimination or What? How Many Variables in the Prediction Formula? Alternatives to Using Subsets 'Black Box' Use of Best-Subsets Techniques L...
Publication Info
- Year
- 1949
- Type
- article
- Volume
- 44
- Issue
- 245
- Pages
- 32-61
- Citations
- 1393
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
1393
OpenAlex
Cite This
Donald Cochrane,
Guy H. Orcutt
(1949).
Application of Least Squares Regression to Relationships Containing Auto-Correlated Error Terms.
Journal of the American Statistical Association
, 44
(245)
, 32-61.
https://doi.org/10.1080/01621459.1949.10483290
Identifiers
- DOI
- 10.1080/01621459.1949.10483290