Modeling the diffusion of scientific publications

This paper illustrates that salient features of a panel of time series of annual citations can be captured by a Bass type diffusion model. We put forward an extended version of this diffusion model, where we consider the relation between key characteristics of the diffusion process and features of the articles. More specifically, parameters measuring citations' ceiling and the timing of peak citations are correlated with specific features of the articles like the number of pages and the number of authors. Our approach amounts to a multi-level non-linear regression for a panel of time series. We illustrate our model for citations to articles that were published in Econometrica and the Journal of Econometrics. Amongst other things, we find that more references lead to more citations and that for the Journal of Econometrics peak citations of more recent articles tend to occur later.diffusion of innovations;multi-level regression

Impulse-response analysis of the market share attraction model

We propose a simulation-based technique to calculate impulse-response functions and their confidence intervals in a market share attraction model [MCI]. As an MCI model implies a reduced form model for the logs of relative market shares, simulation techniques have to be used to obtain the impulse-responses for the levels of the market shares. We apply the technique to an MCI model for a five-brand detergent market. We illustrate how impulse-response functions can help to interpret the estimated model. In particular, the competitive and dynamic structure of the model can be analyzed.market shares;forecasting;attraction models;impulse-response analysis

Moderating Factors of Immediate, Dynamic, and Long-run Cross-Price Effects

In this article the authors describe their comprehensive analysis of moderating factors of cross-brand effects of price changes and contribute to the literature in five major ways. (1) They consider an extensive set of potential variables influencing cross-brand effects of price changes. (2) They examine moderators for the immediate as well as the dynamic cross-price effect. (3) They decompose price into regular and promotional price and study both cross-price effects separately. (4) They compare their findings with previous literature on the moderating factors of own-price effects to understand which factors influence own-price elasticity through affecting brand switching. (5) The authors use an advanced Bayesian estimation technique. The results show evidence of the neighborhood price effect and suggest that it is conditional on whether the promoted brand is priced above or below its competitor. The promoted brand's activities turn out to play a much more important role in determining the cross-price promotional effects than its competitor's similar activities. The authors outline conditions when cross-brand post-promotion dips tend to occur. Finally, they argue that the brand choice portion of the overall own-brand effect of a promotion depends on the brand's marketing strategy and on category-specific characteristics.dynamic effects;asymmetry;hierarchical Bayes;cross-price elasticity

Modeling dynamic effects of promotion on interpurchase times

In this paper we put forward a duration model to analyze the dynamic effects of marketing-mix variables on interpurchase times. We extend the accelerated failure-time model with an autoregressive structure. An important feature of our model is that it allows for different long-run and short-run effects of marketing-mix variables on interpurchase times. As marketing efforts usually change during the spells, we explicitly deal with time-varying covariates. Our empirical analysis of purchases in three different categories reveals that, for some segments of households, the short-run effects of marketing-mix variables are significantly different from the long-run effects.Dynamic duration model;Error-correction model;Time-varying covariates;Unobserved heterogeneity

Modeling Dynamic Effects of the Marketing Mix on Market Shares

To comprehend the competitive structure of a market, it is important to understand the short-run and long-run effects of the marketing mix on market shares. A useful model to link market shares with marketing-mix variables, like price and promotion, is the market share attraction model. In this paper we put forward a representation of the attraction model, which allows for explicitly disentangling long-run from short-run effects. Our model also contains a second level, in which these dynamic effects are correlated with various brand and product category characteristics.Based on the findings in for example Nijs et al. (2001), we postulate the expected signs of these correlations. We fit our resultant Hierarchical Bayes attraction model to data on seven categories in two geographical areas. This data set spans a total of 50 brands. Our main finding is that, in absolute sense, the short-run price elasticity usually exceeds the long-run effect. Moreover, we find that the longrun price effects are strongly correlated with relative price and coupon intensity of a brand.market shares;marketing mix;hierarchical bayes;long-term effects

Ordered logit analysis for selectively sampled data

When customers are classified into ordered categories, which are defined from the outset, it may happen that the majority belongs to a single category. If a market researcher is interested in the correlation between the classification and individual characteristics, the natural question is whether one needs to collect data for all customers in that particular category. We address this question for the ordered logit model. We show that there is no need to consider all those customers. All that is required is a simple modification of the log-likelihood, which is based on Bayes' rule. We illustrate our proposed method on simulated data and on data concerning risk profiles of customers of an investment bank.ordered logit model;selective sampling;Bayes' rule

Incorporating Responsiveness to Marketing Efforts When Modeling Brand Choice

In this paper we put forward a brand choice model which incorporates responsiveness to marketing efforts as a form of structural heterogeneity. We introduce two latent segments of households. The households in the first segment are assumed to respond to marketing efforts while households in the second segment do not do so. Whether a specific household is a member of the first or the second segment at a specific purchase occasion is described by household-specific characteristics and characteristics concerning buying behavior. Households may switch between responsiveness states over time.We compare the in- and out-of-sample performance of our model with various versions of the MNL model. We conclude that, while using the smallest amount of parameters, our model outperforms all MNL variants on forecasting. This, together with the face validity of our parameter results, leads us to believe that incorporating responsiveness seems to be a worthwhile exercise.mixtures;Marketing-instrument effectiveness;multinomial logit;state dependence;structural heterogeneity

Performance of Seasonal Adjustment Procedures: Simulation and Empirical Results

In this chapter we use a simulation experiment to examine whether theseasonal adjustment methods Census X12-ARIMA and TRAMO/SEATS effectivelyremove seasonality properties from time series data, while preserving otherfeatures like the stochastic trend. As data generating processes we use avariety of processes that are actually found in practice. These processesinclude constant seasonality, changing seasonal patterns due to seasonalunit roots and processes with periodically varying parameters. To check forseasonality, we consider tests for seasonal unit roots, for deterministicseasonality, for seasonality in the variance, and for periodicity in theparameters. Our simulation results show that both adjustment methods areable to remove stochastic seasonal patterns from the data with the exceptionof changing seasonal patterns due to periodicity in the parameters. Onaverage, the two methods perform equally well.

Econometric Analysis of the Market Share Attraction Model

Market share attraction models are useful tools for analyzing competitive structures. The models can be used to infer cross-effects of marketing-mix variables, but also the own effects can be adequately estimated while conditioning on competitive reactions. Important features of attraction models are that they incorporate that market shares sum to unity and that the market shares of individual brands are in between 0 and 1. Next to analyzing competitive structures, attraction models are also often considered for forecasting market shares. The econometric analysis of the market share attraction model has not received much attention. Topics as specification, diagnostics, estimation and forecasting have not been thoroughly discussed in the academic marketing literature. In this chapter we go through a range of these topics, and, along the lines, we indicate that there are ample opportunities to improve upon present-day practice.model selection;forecasting;Market share attraction model;diagnostics;estimation