Text

Estimating disaggregate models using aggregate data through augmentation of individual choice

---

In this article, the authors propose a Bayesian method for estimating disaggregate choice models using aggregate data. Compared with existing methods, the advantage of the proposed method is that it allows for the analysis of microlevel consumer dynamic behavior, such as the impact of purchase history on current brand choice, when only aggregate-level data are available. The essence of this approach is to simulate latent choice data that are consistent with the observed aggregate data. When the augmented choice data are made available in each iteration of the Markov chain Monte Carlo algorithm, the dynamics of consumer buying behavior can be explicitly modeled. The authors first demonstrate the validity of the method with a series of simulations and then apply the method to an actual store-level data set of consumer purchases of refrigerated orange juice. The authors find a significant amount of dynamics in consumer buying behavior. The proposed method is useful for managers to understand better the consumer purchase dynamics and brand price competition when they have access to aggregate data only..Printed Journal

---

Ketersediaan

Call Number	Location	Available
	PSB lt.dasar - Pascasarjana	1

Penerbit	American Marketing Association.,
Edisi	-
Subjek	Consumer behavior Brand Preferences studies Estimating techniques Bayesian analysis Markov analysis
ISBN/ISSN	222437
Klasifikasi	-
Deskripsi Fisik	-
Info Detail Spesifik	-
Other Version/Related	Tidak tersedia versi lain
Lampiran Berkas	Tidak Ada Data