On the Bass diffusion theory, empirical models and out-of-sample forecasting

The Bass (1969) diffusion theory often guides the construction of forecasting models for new product diffusion. To match the model with data, one needs to put forward a statistical model. This paper compares four empirical versions of the model, where two of these explicitly incorporate autoregressive dynamics. Next, it is shown that some of the regression models imply multi-step ahead forecasts that are biased. Therefore, one better relies on the simulation methods, which are put forward in this paper. An empirical analysis of twelve series (Van den Bulte and Lilien 1997) indicates that one-step ahead forecasts substantially improve by including autoregressive terms and that simulated two-step ahead forecasts are quite accurate.forecasting;diffusion

The Error of Prediction for a Simultaneous Equation Model

One of the most important functions of a simultaneous equation model is prediction the values of endogenous variables given the values of the predetermined variables and a lot of work has been done to estimate the accuracy of such predictions. Hooper and Zellner (1961) obtained the covariance matrix of the prediction error for unrestricted reduced form and Goldberger, Nagarand Odeh (1961) derived one for restricted reduced form. Properties of predictions for partially restricted reduced form have been analyzed by Amemiya (1966), Kakwani and Court (1972) and Nagar and Sahay (1978). The comparison of these estimators in the context of prediction has been carried on by Dhrymes (1973) and Park (1982). However all these derivations are made forreduced forms of correctly specified linear simultaneous equation models and they still remainunknown for the under and the over specified models. The purpose of this paper is to derive thematrices of the mean squared prediction error for both the underfitted and the overfitted modelsof unrestricted reduced form of a linear simultaneous equation system. The paper is organized as follows: Section 2 presents the basic model and its assumptions. Sections 3 and 4 derive the matrices of the mean squared prediction error for the underfitted and the overfitted models of unrestricted reduced form respectively. Section 5 gives the conclusions. An appendix contains the proofs of these derivations.prediction;misspecification;simultaneous equations

Estimating duration intervals

Duration intervals measure the dynamic impact of advertising on sales. More precise, the p per cent duration interval measures the time lag between the advertising impulse and the moment that p per cent of its effect has decayed. In this paper, we derive an expression for the duration interval for a general dynamic model linking sales to advertising. Additionally, and this is themain novelty of the paper, we put forward a method to provide confidence bounds around the estimated duration interval. An illustration to real-life data emphasizes its usefulness.marketing;simulation;advertising effects;duration interval

Promising Areas for Future Research on Reverse Logistics: an exploratory study

During the early nineties, the Council of Logistics Management started publishing studieswhere Reverse Logistics was recognized as being relevant both for business and society (Stock,1992). Other studies followed stressing the opportunities on reuse and recycling (Kopicki etal., 1993), discussing marketing aspects (Kostecki, 1998) and reported on the U.S. experience(Rogers and Tibben-Lembke, 1999). In Europe, an inter-university EU sponsored projectcalled RevLog had served as one of the motors for European Research on Reverse Logistics.For the last 5 years, researchers associated with RevLog have co-authored more than 100papers on the subject (see Dekker et al., 2003). Very recently, the RevLog group organizeda meeting to identify ?Promising Areas for Future Research on Reverse Logistics.? In thispaper we report the outcome of such meeting.reverse logistics;exploratory study;future;nominal group technique

A Lotting Method for Electronic Reverse Auctions

An increasing number of commercial companies are using online reverse auctions for their sourcing activities. In reverse auctions, multiple suppliers bid for a contract from a buyer for selling goods and/or services. Usually, the buyer has to procure multiple items, which are typically divided into lots for auctioning purposes. By steering the composition of the lots, a buyer can increase the attractiveness of its lots for thesuppliers, which can then make more competitive offers, leading to larger savings for the procuring party. In this paper, a clustering-based heuristic lotting method is proposed for reverse auctions. Agglomerative clustering is used for determining the items that will be put in the same lot. A suitable metric is defined, which allows the procurer to incorporate various approaches to lotting. The proposed lotting method has been tested for the procurement activities of a consumer packaged goods company. The results indicate that the proposed strategy leads to 2-3% savings, while the procurement experts confirm that the lots determined by the proposed method are acceptable given the procurement goals.e-commerce;reverse auctions;hierarchical clustering;lotting;e-procurement

A note on a multi-period profit maximizing model for retail supply chain management

In this note we present an efficient exact algorithm to solve the joint pricing and inventoryproblem for which Bhattacharjee and Ramesh (2000) proposed two heuristics. Our algorithmappears to be superior also in terms of computation time. Furthermore, we point out several mistakes in the paper by Bhattacharjee and Ramesh.pricing;inventory;dynamic programming

Induction of Ordinal Decision Trees

This paper focuses on the problem of monotone decision trees from the point of view of the multicriteria decision aid methodology (MCDA). By taking into account the preferences of the decision maker, an attempt is made to bring closer similar research within machine learning and MCDA. The paper addresses the question how to label the leaves of a tree in a way that guarantees the monotonicity of the resulting tree. Two approaches are proposed for that purpose - dynamic and static labeling which are also compared experimentally. The paper further considers the problem of splitting criteria in the con- text of monotone decision trees. Two criteria from the literature are com- pared experimentally - the entropy criterion and the number of con criterion - in an attempt to find out which one fits better the specifics of the monotone problems and which one better handles monotonicity noise.monotone decision trees;noise;multicriteria decision aid;multicriteria sorting;ordinal classication

Fat Tails in Power Prices

Spot power prices exhibit extreme price jumps and the tendency to oscillate around a long-term mean. Despite these well-known characteristics, electricity price models used for Monte Carlo simulations, VaR related measures, or derivatives valuation, often assume normally distributed residuals. In this paper, we examine the distributional characteristics of model residuals and show that the hypothesis of normality is rejected due to significant tail fatness and skewness. We then examine the Student-t distribution as a candidate fit for residuals and as an alternative distribution for random innovations in Monte Carlo simulations. The resulting price patterns clearly show that simulations based on the Student-t distribution resemble more closely actual power price patters. We then discuss the implications of our results for risk management.modelling;risk management;extreme value theory;Monte Carlo simulations;electricity price;spikes

Some Comments on the Question Whether Co-Occurrence Data Should Be Normalized

In a recent article in JASIST, L. Leydesdorff and L. Vaughan (2006) asserted that raw cocitation data should be analyzed directly, without first applying a normalization such as the Pearson correlation. In this communication, it is argued that there is nothing wrong with the widely adopted practice of normalizing cocitation data. One of the arguments put forward by Leydesdorff and Vaughan turns out to depend crucially on incorrect multidimensional scaling maps that are due to an error in the PROXSCAL program in SPSS.multidimensional scaling;PROXSCAL;Pearson correlation;author cocitation analysis;co-occurrence data;normalization