Modeling competition between two pharmaceutical drugs using innovation diffusion models

The study of competition among brands in a common category is an interesting strategic issue for involved firms. Sales monitoring and prediction of competitors' performance represent relevant tools for management. In the pharmaceutical market, the diffusion of product knowledge plays a special role, different from the role it plays in other competing fields. This latent feature naturally affects the evolution of drugs' performances in terms of the number of packages sold. In this paper, we propose an innovation diffusion model that takes the spread of knowledge into account. We are motivated by the need of modeling competition of two antidiabetic drugs in the Italian market.Comment: Published at http://dx.doi.org/10.1214/15-AOAS868 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

"Rotterdam econometrics": publications of the econometric institute 1956-2005

This paper contains a list of all publications over the period 1956-2005, as reported in the Rotterdam Econometric Institute Reprint series during 1957-2005.

Multi-scale Attention Flow for Probabilistic Time Series Forecasting

The probability prediction of multivariate time series is a notoriously challenging but practical task. On the one hand, the challenge is how to effectively capture the cross-series correlations between interacting time series, to achieve accurate distribution modeling. On the other hand, we should consider how to capture the contextual information within time series more accurately to model multivariate temporal dynamics of time series. In this work, we proposed a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF), where we integrate multi-scale attention and relative position information and the multivariate data distribution is represented by the conditioned normalizing flow. Additionally, compared with autoregressive modeling methods, our model avoids the influence of cumulative error and does not increase the time complexity. Extensive experiments demonstrate that our model achieves state-of-the-art performance on many popular multivariate datasets

A new multivariate product growth model

To examine cross-country diffusion new products, marketing researchers have to rely on a multivariate product growth model. We put forward such a model, and show that it is a natural extension of the original Bass (1969) model. We contrast our model with currently in use multivariate models and we show that inference is much easier and inter- pretation is straightforward. Especially if the number of countries is larger than two. In fact, parameter estimation can be done using standard commercially available software. We illustrate the beneffits our model relative to other models in simulation experiments. These experiments show that in the competing models the cross-country effects are actu- ally very difficult o identify from the data. An application to a three-country CD sales series shows the merits of our model in practice. Keywords: Diffusion, international marketing, econometric models JEL: M31, C3

Application of Shallow Neural Networks to Retail Intermittent Demand Time Series

Accurate sales predictions are essential for businesses in the fast-moving consumer goods (FMCG) industry. However, their demand forecasts are often unreliable, leading to imprecisions that affect downstream decisions. This dissertation proposes using an artificial neural network to improve intermittent demand forecasting in the retail sector. The research investigates the validity of using unprocessed historical information, eluding hand-crafted features, to learn patterns in intermittent demand data. The experiment tests a selection of shallow neural network architectures that can expedite the time-to-market in comparison to conventional demand forecasting methods. The results demonstrate that organisations that still rely on manual and direct forecasting methods could improve their predicting accuracy and establish a high-performing baseline for future development. The solution also offers an end-to-end systematic forecasting landscape enabling a lift-and-shift and easy transition from design to deployment. A practical implementation should bring about stable and reliable forecasts, resulting in cost savings, improved customer service, and increased profitability. Lastly, the research findings contribute to the broader academic field of forecasting and ML with a seminal proposal that provides insights and opportunities for future research

The Deskilling vs Upskilling Debate: The Role of BLS Projections

[Excerpt] The growing shortage of professionally trained workers and the rising skill premiums will tend to cause supply to increase more rapidly than we have projected. But the gap between the projected growth of demand and supply is huge. Just to maintain the balance between the growth of supply and the growth of occupational demand that prevailed in the 1980s, itself a period of shortage, it will be necessary to increase in the stock of college graduates in the year 2000 by 3.7 million or, put another way, to raise the number of college graduates entering the labor forces by 462,000 or 42 percent between 1992 and the year 2000

Time Series Event Forecasting in Consumer Electronic Markets using Random Forests

Consumers are price-sensitive and opportunistic about the place of purchase when buying electronic goods. However, services that advise customers on their purchase time decisions for those products are missing. Given the objective to provide a binary signal to customers to either wait or purchase immediately, classification algorithms are a direct methodological choice. Approaches like random forests allow for the derivation of a probability and class prediction but are usually not used in time series contexts. This is due to missing or time-invariant regressors and unclear prediction settings. We show how classification methods can be used to generate reliable predictions of price events and analyze if they are subject to common market dependencies. Pooling univariate random forests and enhancing them with multivariate features shows that our approach generates stable and valuable recommendations. Because dependency structures between products are transferable, multivariate forecasting increases accuracy and issues recommendations where univariate approaches fail