scholarly journals Analyzing the demand system for beer in the US: an application of the random coefficient logit model using scanner data

2020 ◽  
Author(s):  
◽  
Han Wang
Keyword(s):  
Logit Model ◽  
Random Coefficient ◽  
Demand System ◽  
Scanner Data ◽  
The Us ◽  
Random Coefficient Logit

Simulated Maximum Likelihood Estimator for the Random Coefficient Logit Model Using Aggregate Data

2009 ◽  
Vol 46 (4) ◽  
pp. 531-542 ◽  
Author(s):  
Sungho Park ◽  
Sachin Gupta
Keyword(s):  
Maximum Likelihood ◽  
Logit Model ◽  
Regime Switching ◽  
Control Function ◽  
Sampling Error ◽  
Estimation Method ◽  
Aggregate Data ◽  
Random Coefficient ◽  
Simulated Maximum Likelihood ◽  
Random Coefficient Logit

The authors propose a simulated maximum likelihood estimation method for the random coefficient logit model using aggregate data, accounting for heterogeneity and endogeneity. The method allows for two sources of randomness in observed market shares: unobserved product characteristics and sampling error. Because of the latter, the method is suitable when sample sizes underlying the shares are finite. In contrast, Berry, Levinsohn and Pakes's commonly used approach assumes that observed shares have no sampling error. The method can be viewed as a generalization of Villas-Boas and Winer's approach and is closely related to Petrin and Train's “control function” approach. The authors show that the proposed method provides unbiased and efficient estimates of demand parameters. They also obtain endogeneity test statistics as a by-product, including the direction of endogeneity bias. The model can be extended to incorporate Markov regime-switching dynamics in parameters and is open to other extensions based on maximum likelihood. The benefits of the proposed approach are achieved by assuming normality of the unobserved demand attributes, an assumption that imposes constraints on the types of pricing behaviors that are accommodated. However, the authors find in simulations that demand estimates are fairly robust to violations of these assumptions.

scholarly journals Quantifying the Economic Value of Warranties: A Survey

2014 ◽  
Vol 2 (1) ◽  
pp. 75-94 ◽  
Author(s):  
Ede Lázár
Keyword(s):  
Logit Model ◽  
Recent Literature ◽  
Economic Value ◽  
Demand System ◽  
Differentiated Products ◽  
Demand Functions ◽  
On Demand ◽  
Economic Rationale ◽  
The Impact ◽  
Random Coefficient Logit

Abstract This paper is a review of the most recent literature regarding the econometric modelling of the impact of warranties on demand. The reviewed literature is limited to the papers that apply the random-coefficient logit model based on Berry, Levinsohn and Pakes (1995) to estimate differentiated products demand. An important feature of these demand system models that is a clear advantage to earlier demand functions is to account for the endogeneity of prices. We focus on those model specifications that take into account endogeneity of both prices and warranty. Another goal for modelling the effect of warranties is to explore the economic rationale for warranty provision. Four theories have been proposed in the literature: insurance, sorting, signalling and incentive theories. This paper aims at decomposing the effect of these theories, to account for different underlying assumptions and to separately determine the implications as presented in the recent literature

scholarly journals Challenges in Merger Simulation Analysis

2011 ◽  
Vol 101 (3) ◽  
pp. 56-59 ◽  
Author(s):  
Christopher R Knittel ◽  
Konstantinos Metaxoglou
Keyword(s):  
Logit Model ◽  
Simulation Analysis ◽  
Consumer Welfare ◽  
Random Coefficient ◽  
Supply Side ◽  
Demand Side ◽  
Market Outcomes ◽  
Starting Values ◽  
Bertrand Game ◽  
Random Coefficient Logit

In this paper, we share our experience with merger simulations using a Random Coefficient Logit model on the demand side and assuming a static Bertrand game on the supply side. Drawing largely from our work in Knittel and Metaxoglou (2008), we show that different demand estimates obtained from different combinations of optimization algorithms and starting values lead to substantial differences in post-merger market outcomes using metrics such as industry profits, and change in consumer welfare and prices.

scholarly journals Studying composite demand using scanner data: the case of ground beef in the US

2012 ◽  
Vol 43 ◽  
pp. 49-57 ◽  
Author(s):  
Lee L. Schulz ◽  
Ted C. Schroeder ◽  
Tian Xia
Keyword(s):  
Ground Beef ◽  
Scanner Data ◽  
The Us

scholarly journals Monte Carlo Simulation in Random Coefficient Logit Models Involving Large Sums

2013 ◽  
Vol 1 (1) ◽  
pp. 85-108
Author(s):  
Zsolt Sándor
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Mean Squared Error ◽  
Mixed Logit ◽  
Random Coefficient ◽  
Consumer Information ◽  
Logit Models ◽  
Quasi Monte Carlo ◽  
Random Coefficient Logit

Abstract We study Monte Carlo simulation in some recent versions of random coefficient logit models that contain large sums of expressions involving multivariate integrals. Such large sums occur in the random coefficient logit with demographic characteristics, the random coefficient logit with limited consumer information and the design of choice experiments for the panel mixed logit. We show that certain quasi-Monte Carlo methods, that is, so-called (t, m, s)-nets, provide improved performance over pseudo-Monte Carlo methods in terms of bias, standard deviation and root mean squared error.

Do Household Scanner Data Provide Representative Inferences from Brand Choices: A Comparison with Store Data

1996 ◽  
Vol 33 (4) ◽  
pp. 383-398 ◽  
Author(s):  
Sachin Gupta ◽  
Pradeep Chintagunta ◽  
Anil Kaul ◽  
Dick R. Wittink
Keyword(s):  
Logit Model ◽  
Choice Behavior ◽  
Selection Procedure ◽  
Brand Choice ◽  
Scanner Data ◽  
Multinomial Model ◽  
Household Data ◽  
Estimated Parameters ◽  
Statistical Support ◽  
Household Heterogeneity

The authors investigate whether household scanner data provide representative inferences about brand choice behavior. They show the (analytical) equivalence of the homogeneous brand choice logit and the multinomial store sales models. Then, they determine how results for the homogeneous logit model estimated with actual household data deviate from results for the multinomial model estimated with actual store data from the same community. They also perform this comparison using model specifications that provide average estimated parameters while accommodating unobserved household heterogeneity. The authors find statistical support for the hypothesis that panelist households are not representative, whether household heterogeneity is or is not accommodated. Substantively, however, the average estimated price elasticities are close for the household and store data analyzed, if the household data are selected on the basis of purchase selection. An alternative selection procedure, called household selection, which is used for the analysis of complete household purchase records, provides results that strongly differ from the purchase selection results.

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