A simple recursive estimation method for linear regression models with AR(p) disturbances

Badi H. Baltagi; Qi Li

doi:10.1007/BF02926403

A simple recursive estimation method for linear regression models with AR(p) disturbances

Badi H. Baltagi, Qi Li

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

An exact transformation that reduces the AR(p) process into white noise is well known in statistics, see Fuller (1976). However, practitioners still use and econometric textbooks still recommend the Cochrane-Orcutt procedure for p>2, see Greene (1990). This paper derives an alternative exact transformation for the AR(p) process which is computationally simple. Based on this transformation, a GLS estimator is proposed, requiring only least squares regressions and recursive computations. This is illustrated for the AR(3) case.

Original language	English (US)
Pages (from-to)	93-100
Number of pages	8
Journal	Statistical Papers
Volume	35
Issue number	1
DOIs	https://doi.org/10.1007/BF02926403
State	Published - Dec 1994
Externally published	Yes

Keywords

Autocorrelation Transformation
Autoregressive Process
Serial Correlation

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1007/BF02926403

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abstract = "An exact transformation that reduces the AR(p) process into white noise is well known in statistics, see Fuller (1976). However, practitioners still use and econometric textbooks still recommend the Cochrane-Orcutt procedure for p>2, see Greene (1990). This paper derives an alternative exact transformation for the AR(p) process which is computationally simple. Based on this transformation, a GLS estimator is proposed, requiring only least squares regressions and recursive computations. This is illustrated for the AR(3) case.",

keywords = "Autocorrelation Transformation, Autoregressive Process, Serial Correlation",

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AU - Baltagi, Badi H.

AU - Li, Qi

PY - 1994/12

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N2 - An exact transformation that reduces the AR(p) process into white noise is well known in statistics, see Fuller (1976). However, practitioners still use and econometric textbooks still recommend the Cochrane-Orcutt procedure for p>2, see Greene (1990). This paper derives an alternative exact transformation for the AR(p) process which is computationally simple. Based on this transformation, a GLS estimator is proposed, requiring only least squares regressions and recursive computations. This is illustrated for the AR(3) case.

AB - An exact transformation that reduces the AR(p) process into white noise is well known in statistics, see Fuller (1976). However, practitioners still use and econometric textbooks still recommend the Cochrane-Orcutt procedure for p>2, see Greene (1990). This paper derives an alternative exact transformation for the AR(p) process which is computationally simple. Based on this transformation, a GLS estimator is proposed, requiring only least squares regressions and recursive computations. This is illustrated for the AR(3) case.

KW - Autocorrelation Transformation

KW - Autoregressive Process

KW - Serial Correlation

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A simple recursive estimation method for linear regression models with AR(p) disturbances

Abstract

Keywords

ASJC Scopus subject areas

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