A Logit Model of Brand Choice Calibrated on Scanner Data

Guadagni, Peter M.; Little, John D. C.

doi:10.1287/mksc.2.3.203

Cited by 1,543 publications

(491 citation statements)

References 15 publications

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“…Maximization of L (β, ψ) is intractable due to the presence of the q-dimensional integral J (β, ψ) in equation (7). Several methods have been proposed for circumventing this problem.…”

Section: Estimation Of Gammsmentioning

confidence: 99%

“…Gaussian, Logit, and Poisson regression models are among the most widely used GLMs. Common applications of Logit models include analysis of brand choice data in marketing (Baltas [2] and Guadagni and Little [7]) and transportation choice data in economics (Greene [6] and Manski and McFadden [12]). Applications of Poisson regression models include analysis of data on patents, number of trips to a doctor's office, and number of shipping accidents.…”

Section: Introductionmentioning

confidence: 99%

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Semi-parametric mixed effects models for longitudinal data with applications in business and economics

Sapra¹

2014

IJASP

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Longitudinal data is becoming increasingly common in business, social sciences, and biological sciences due to the advantages it offers over cross-section data in modeling and incorporating heterogeneity among subjects and in being able to make causal inferences from observational data. Parametric models and methods are widely used for analyzing longitudinal data for continuous, discrete, and count data occurring in these disciplines. Some popular models are Gaussian, Logit, and Poisson fixed and random effects models. These models are unreliable in situations in which the link function is nonlinear and the form of nonlinearity is not known with certainty. This paper employs a semiparametric extension of fixed and random effects models called generalized additive mixed models (GAMMs) to analyze several longitudinal data sets. These semi-parametric models are flexible and robust extensions of generalized linear models. Following Wood [19], the GAMMs are represented using penalized regression splines and estimated by penalized regression methods treating the penalized component of each smooth as a random effect term and the unpenalized component as a fixed effect term. The degree of smoothness for the unknown functions in the linear predictor part of the GAMM is estimated as the variance parameter of the term. Applications of GAMMs studied include analysis of anti-social behavior, decision to use a professional tax preparer, and analysis of patent data on manufacturing firms. For each application, several GAMMs are compared with their parametric counterparts.

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“…Maximization of L (β, ψ) is intractable due to the presence of the q-dimensional integral J (β, ψ) in equation (7). Several methods have been proposed for circumventing this problem.…”

Section: Estimation Of Gammsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semi-parametric mixed effects models for longitudinal data with applications in business and economics

Sapra¹

2014

IJASP

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“…Most of these decision support models utilize the logit model to characterize consumer choice. Tellis and Zufryden (1995) use a Guadagni and Little (1983) type logit model where cross-sectional consumer heterogeneity is captured by means of brand loyalty variable and temporal consumer heterogeneity is captured by the lagged choice. Silva-Risso et al (1999) rely on a latent class logit model to characterize heterogeneity in consumer response parameters.…”

Section: Dollarmetric Evaluation Of Modeling Consideration Setsmentioning

confidence: 99%

A Framework to Determine the Value of Consumer Consideration Set Information for Firm Pricing Strategies

Pancras

2010

Comput Econ

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“…Many papers have shown the effects of promotions on consumer purchase decision aspects, such as brand choice, purchase time and quantity, brand loyalty and brand switching (see, among others, Bemmaor and Mouchoux, 1991;Ehrenberg, 1972;Ehrenberg et al, 1994;Filippucci and Pacei, 2004;Guadagni and Little, 1983;Gupta, 1988;Inman et al, 1990;Nijs et al, 2001;Schoemaker and Shoaf, 1977). It is beyond the scope of this paper to provide a comprehensive review on this topic; the interested reader may refer to Blattberg et al (1995) and East (1997) for a synthesis of empirical generalizations about promotions and a basic list of references.…”

Section: Positioning Within Literature On Sales Promotionsmentioning

confidence: 99%

The impact of sales promotions on store performance: a structural vector autoregressive approach

Freo

2005

JISS

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The present paper analyses the impact of sales promotions on store performance, in the short and long term, from the retailer's point of view. Relationships among promoted and regular sales in the hypermarkets of a large-scale retail chain of national importance, are investigated by means of a structural vector autoregressive model (SVAR). Statistically significant effects of sales promotions in the heavy household section on store sales are found in the short-run; these promotions produce additional sales and thus act as an attractive factor. Promotions in textile category, on the contrary, produce an immediate negative effect on net sales. In the long run, negative statistically significant effects on regular sales are detected when promotions are repeatedly implemented within perishables category.

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A Logit Model of Brand Choice Calibrated on Scanner Data

Abstract: ABSTRACT

Cited by 1,543 publications

References 15 publications

Semi-parametric mixed effects models for longitudinal data with applications in business and economics

Semi-parametric mixed effects models for longitudinal data with applications in business and economics

A Framework to Determine the Value of Consumer Consideration Set Information for Firm Pricing Strategies

The impact of sales promotions on store performance: a structural vector autoregressive approach

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