Logically Consistent Market Share Models

Naert, Philippe A.; Bultez, Alain

doi:10.2307/3149706

Cited by 47 publications

(16 citation statements)

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“…. u~ Naert and Bultez (1973) show that the shares m,i add up to 1 at maximum likelihood estimates to be unstable or even making it every point in time if and only if:…”

Section: The Model and The Estimation Proceduresmentioning

confidence: 99%

Maximum likelihood estimation of market share models with large numbers of shares

Boer

Harkema

Soede

1996

Applied Economics Letters

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Maximum likelihood procedures for estimating market share models break down or produce very unstable estimates when the number of brands is large as compared with the number of observations. The reason behind this phenomenon is that the estimate of the contemporaneous covariance matrix of the error terms of the model becomes singular or almost singular. This problem may be resolved by imposing restrictions on the contemporaneous covariance matrix. The resulting estimation procedure suggests that the model may contain a large number of shares, while the variance of each share may be estimated freely.

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“…. u~ Naert and Bultez (1973) show that the shares m,i add up to 1 at maximum likelihood estimates to be unstable or even making it every point in time if and only if:…”

Section: The Model and The Estimation Proceduresmentioning

confidence: 99%

Maximum likelihood estimation of market share models with large numbers of shares

Boer

Harkema

Soede

1996

Applied Economics Letters

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show abstract

“…9 Throughout the model development, for simplicity of exposition, we attempt to express key probabilities as products of factors in the unit interval. This not only allows a compact description of the various effects but also ensures logical consistency, in Naert and Bultez's (1973) sense.…”

Section: Consumer Choice In the Absence Of Promotionsmentioning

confidence: 99%

Journal of Marketing Research

2010

Journal of Marketing Research

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“…Constraints for the generallinear model (with constantterm) were derived by Schmalensee (1972) and Naert and Bultez (1973). See also McGuire and Weiss (1976) and Weverbergh (1976).…”

Section: Can Non-robust Models Be Good Models?"mentioning

confidence: 99%

“…Such models are then called logically consistent (see, for example, Naert and Bultez, 1973). In a broader sense, robustness may also mean that a model should reflect:…”

Section: Models Should Be Robustmentioning

confidence: 99%