A Note on 'Bayesian analysis of the random coefficient model using aggregate data', an alternative approach

In this note on the paper from (Jiang, Manchanda & Rossi 2009) I want to discuss a simple alternative estimation method of the multinomial logit model for aggregated data, the so called BLP model, named after (Berry, Levinsohn & Pakes 1995). The estimation is conducted through a bayesian estimation similar to (Jiang et al. 2009). But in difference to them here the time intensive contraction mapping for assessing the mean utility in every iteration step of the estimation procedure is not needed. This is because the likelihood function is computed via a special case of the control function method ((Petrin & Train 2002) and (Park & Gupta 2009)) and hence a full random walk MCMC algorithm is applied. In difference to (Park & Gupta 2009) the uncorrelated error, which is explicitly introduced through the control function procedure, is not integrated out, but sampled with a random walk MCMC. The introduced proceeding enables to use the whole information from the data set in the estimation and beyond that accelerates the computation

A Note on 'Bayesian analysis of the random coefficient model using aggregate data', an alternative approach

In this note on the paper from (Jiang, Manchanda & Rossi 2009) I want to discuss a simple alternative estimation method of the multinomial logit model for aggregated data, the so called BLP model, named after (Berry, Levinsohn & Pakes 1995). The estimation is conducted through a bayesian estimation similar to (Jiang et al. 2009). But in difference to them here the time intensive contraction mapping for assessing the mean utility in every iteration step of the estimation procedure is not needed. This is because the likelihood function is computed via a special case of the control function method ((Petrin & Train 2002) and (Park & Gupta 2009)) and hence a full random walk MCMC algorithm is applied. In difference to (Park & Gupta 2009) the uncorrelated error, which is explicitly introduced through the control function procedure, is not integrated out, but sampled with a random walk MCMC. The introduced proceeding enables to use the whole information from the data set in the estimation and beyond that accelerates the computation.Bayesian estimation, random coefficient logit, aggregate share models

Search Engine Advertising Effectiveness in a Multimedia Campaign

Search engine advertising has become a multibillion-dollar business and one of the dominant forms of advertising on the Internet. This study examines the effectiveness of search engine advertising within a multimedia campaign, with explicit consideration of the interaction effects between search engine advertising and television and banner advertising. An advertising tracking study with about 300 respondents interviewed before and about 4,700 respondents interviewed after the advertising campaign examines the effects on four consumer metrics: advertising awareness, brand awareness, brand image, and brand consumption. We estimate advertising effectiveness and control for correlations across the four ordinal response metrics using a multivariate ordered probit model. The results show that search engine advertising has significant effects on several consumer metrics, even among consumers who do not click on the sponsored advertisement. Television advertising also affects the consumer metrics. However, a negative interaction effect emerges between search engine advertising and television advertising. Banner advertising exerts a positive impact, but only in combination with television advertising. These substantial interaction effects indicate that firms must consider the investments in various media channels simultaneously when they design multimedia campaigns