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Abstract:
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, long-term effects, hierarchical bayes

Abstract:
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, long-term effects, hierarchical bayes

Abstract:
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.

Market share attraction model, model selection, estimation, diagnostics, forecasting

Abstract:
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.

Marketing-instrument effectiveness, structural heterogeneity, state dependence, multinomial logit, mixtures

Abstract:
The authors put forward a sales response model to explain the differences in immediate and dynamic effects of promotional prices and regular prices on sales. The model consists of a vector autoregression rewritten in error-correction format which allows to disentangle the immediate effects from the dynamic effects. In a second level of the model, the immediate price elasticities, the cumulative promotional price elasticity and the long-run regular price elasticity are correlated with various brand-speciffic and category-speciffic characteristics. The model is applied to seven years of data on weekly sales of 100 different brands in 25 product categories. We find many significant moderating effects on the elasticity of price promotions. Brands in categories that are characterized by high price differentiation and that constitute a lower share of budget are less sensitive to price discounts. Deep price discounts turn out to increase the immediate price sensitivity of customers. We also find significant effects for the cumulative elasticity. The immediate effect of a regular price change is often close to zero. The long-run effect of such a decrease usually amounts to an increase in sales. This is especially true in categories characterized by a large price dispersion, frequent price promotions and hedonic, non-perishable products.

sales, vector autoregression, marketing mix, promotional and regular price, short and long-term effects, hierarchical bayes

Abstract:
A commonly applied modeling tool for the analysis of promotional effects onweekly sales data is a linear regression model. Usually, such a model includes0/1 dummy variables for promotions, where weeks with a promotion get a valueof 1. When these variables are included in a model with parameters which areconstant over time, the market researcher implicitly makes two important but ratherrestrictive assumptions. The first is that anytime a dummy variable takes a value of1 and the relevant parameter is significant, there is a non-zero effect of promotionon sales. The second is that this effect is constant across all weeks.In many practical cases however, one may conjecture that the effects of promo-tion are not constant over time. Therefore, we propose a new and rather parsimo-nious econometric model for the purpose of measuring the effects of promotions,while allowing for time-variation in these effects. The main idea is that promotionscan (but not necessarily) lead to positive and suddenly large values of sales in thesame week, and that they can perhaps lead to large negative values in the week there-after, if there is a, what is called, post-promotion dip. We discuss representation and interpretation of the model, and we outline the maximum likelihood parameterestimation method. Simulation results suggest that the estimation method is quitereliable and that the distribution of the estimator is approximately normal. Weillustrate the model in substantial detail on two sets of empirical data in order toindicate its practical usefulness

ales, promotions, time-varying effects, censored regression

Abstract:
We propose a consistent utility-based framework to jointly explain a household's decisions on purchase incidence, brand choice and purchase quantity. The approach differs from other approaches, currently available in the literature, as it is able to take into account consumption dynamics. In the model, households derive utility from consumption, and they relate their purchase behavior to consumption planning. We illustrate our model for yogurt purchases, and show that our model yields important additional insights. One such insight is that the reservation price of households is not fixed, but depends on the available inventory stock. Furthermore, we find that promotional activities increase sales through more purchases in the product category and brand switching, but the effect through larger purchase quantities is limited.

purchase incidence, brand choice, purchase quantity, consumption, utility maximization

Abstract:
This paper considers the nature and the distribution of trade and FDI effects of a potential enlargement of the European Monetary Union (EMU) to the ten countries that obtained EU membership in 2004. One-way and two-way error component gravity models are estimated using a dataset of unbalanced panel data that combines bilateral trade flows among 29 countries and the distribution of outward FDI stocks among these countries. The results reveal a complementarity between trade and investment and a relationship between trade and exchange rate volatility that depends on the sign of bilateral trade balances. Using a simulation-based technique, we find that estimates of FDI effects of EMU range between 18.5 percent for Poland and 30 percent for Hungary.

EMU, exchange rate volatility, foreign investment, trade diversion, vertical integration

Abstract:
We propose a new method to model consumers' consideration and choice processes. We develop a parsimonious probit type model for consideration and a multinomial probit model for choice, given consideration. Unlike earlier models of consideration ours is not prone to the curse of dimensionality, while we allow for very general structures of unobserved dependence in consideration among brands. In addition, our model allows for state dependence and marketing mix effects on consideration.Unique to this study is that we attempt to establish the validity of existing practice to infer consideration sets from observed choices in panel data. To this end, we use data collected in an on-line choice experiment involving interactive supermarket shelves and post-choice questionnaires to measure the choice protocol and stated consideration levels. We show with these experimental data that underlying consideration sets can be successfully retrieved from choice data alone and that there is substantial convergent validity of the stated and inferred consideration sets. We further find that consideration is a function of point-of-purchase marketing actions such as display and shelf space, and of consumer memory for recent choices.Next, we estimate the model on IRI panel data. We have three main results. First, compared with the single-stage probit model, promotion effects are larger and are inferred with smaller variances when they are included in the consideration stage of the two-stage model. Promotion effects are significant only in the two-stage model that includes consideration, whereas they are not in a single-stage choice model. Second, the price response curves of the two models are markedly diferent. The two-stage model offers a nice intuition for why promotional price response is different from regular price response. In addition and consistent with intuition, the two-stage model also implies that merchandizing has more effect on choice among those who did not buy the brand before than among those who already did. It is explained why a single-stage model does not harbor this feature. In fact, the single-stage model implies the opposite for smaller or more expensive brands. Third, we find that the consideration of brands does not covary greatly across brands once we take account of observed effects. Managerial implications and future research are also discussed.

Consideration, choice, probit models

Abstract:
It is conceivable that the "whether to buy" and "how much to buy" decisions in the purchasing process of households are influenced by the inventory process. In this paper we therefore put forward a model for consumption, where we rely on established economic theory. We incorporate this model in a model for purchase behavior. Our consumption specification, which is derived from utility maximization principles, is more flexible than an ad hoc approach, which has recently been proposed in the literature. We illustrate our model for yogurt purchases, and show that our model yields important additional and useful insights. One such insight is that promotion anticipation behavior turns out not only to occur in the purchasing process, but also in the consumption process.

consumption function, inventory, utility maximization, promotion anticipation

Abstract:
This paper addresses heterogeneity in determinants of economic growth in a data-driven way. Instead of defining groups of countries with different growth characteristics a priori, based on, for example, geographical location, we use a finite mixture panel model and endogenous clustering to examine cross-country differences and similarities in the effects of growth determinants. Applying this approach to an annual unbalanced panel of 59 countries in Asia, Latin and Middle America and Africa for the period 1971-2000, we can identify two groups of countries in terms of distinct growth structures. The structural differences between the country groups mainly stem from different effects of investment, openness measures and government share in the economy. Furthermore, the detected segmentation of countries does not match with conventional classifications in the literature.

Economic growth, parameter heterogeneities, latent class models, panel time series

Abstract:
We propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: Necessity of a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson (1951), sensitivity to the ordering of the variables for the LDU rank statistic of Cragg and Donald (1996) and Gill and Lewbel (1992), a limiting distribution that is not a standard chi-squared distribution for the rank statistic of Robin and Smith (2000) and usage of numerical optimization for the objective function statistic of Cragg and Donald (1997). The new rank statistic consists of a quadratic form of a (orthogonal) transformation of the smallest singular values of a unrestricted estimate of the matrix of interest. The quadratic form is taken with respect to the inverse of a unrestricted covariance matrix that can be estimated using a heteroscedasticity autocorrelation consistent estimator. The rank statistic has a standard chi-squared limiting distribution. In case of a Kronecker covariance matrix, the rank statistic simplifies to the canonical correlation rank statistic. In the non-stationary cointegration case, the limiting distribution of the rank statistic is identical to that of the Johansen trace statistic. We apply the rank statistic to test for the rank of a matrix that governs the identification of the parameters in the stochastic discount factor model of Jagannathan and Wang (1996). The rank statistic shows that non-identification of the parameters can not be rejected. We further use the stochastic discount factor model to illustrate the validity of the limiting distribution and to conduct a power comparison.

stochastic discount factor model, cointegration, GMM

Abstract:
This paper revisits inflation forecasting using reduced-form Phillips curve forecasts, that is, inflation forecasts that use activity and expectations variables. We propose a Phillips-curve-type model that results from averaging across different regression specifications selected from a set of potential predictors. The set of predictors includes lagged values of inflation, a host of real-activity data, term structure data, nominal data, and surveys. In each individual specification, we allow for stochastic breaks in regression parameters, where the breaks are described as occasional shocks of random magnitude. As such, our framework simultaneously addresses structural change and model uncertainty that unavoidably affect Phillips-curve-based predictions. We use this framework to describe personal consumption expenditure (PCE) deflator and GDP deflator inflation rates for the United States in the post-World War II period. Over the full 1960-2008 sample, the framework indicates several structural breaks across different combinations of activity measures. These breaks often coincide with policy regime changes and oil price shocks, among other important events. In contrast to many previous studies, we find less evidence of autonomous variance breaks and inflation gap persistence. Through a real-time out-of-sample forecasting exercise, we show that our model specification generally provides superior one-quarter-ahead and one-year-ahead forecasts for quarterly inflation relative to an extended range of forecasting models that are typically used in the literature.

inflation forecasting, Phillips correlations, real-time data, structural breaks, model uncertainty, Bayesian model averaging

Abstract:
GARCH models and Stochastic Volatility (SV) models can both be used to describe unobserved volatility in asset returns. We consider the issue of testing a GARCH model against an SV model. For that purpose, we propose a new and parsimonious GARCH-t model with an additional restricted moving average term, which can capture SV model properties. We discuss model representation, parameter estimation, and our simple test for model selection. Furthermore, we derive the theoretical moments and the autocorrelation function of our new model. We illustrate our model and test for nine daily stock-return series.

C22, C52, GARCH, model selection, stochastic volatility

Abstract:
Using the standard linear model as a base, a unified theory of Bayesian Analysis of Cointegration Models is constructed. This is achieved by defining (natural conjugate priors in the linear model and using the implied priors for the cointegration model.

Abstract:
Changing seasonal patterns in economic time series can be described by auregressive models with seasonal unit roots or with deterministic sesaonal mean shifts.By means of simulation we demonstrate the impact of imposing the incorrect model on forecasting. We find for both cases that an inappropriate decision can deteriorate forecasting performance dramatically.

Abstract:
In this paper we show that the answer to the question in the title is affirmative, i.e. seasonal adjustment increases the probabilities in a Markov switching regime model of staying in the same regime. This phenomenon is illustrated through Monte Carlo Simulations and with two examples concerning German unemployment and US industrial production.