Representations of Development in 19th and 20th Century indonesia: A Transport History Perspective

Contemporary debate in Indonesia over ‘people's economy’ and ‘globalisation’ recalls the vigorous 1950s debate over ‘dualism’. Taking as a case study the rise and eclipse of railways, this paper argues that the colonial phenomenon of dualism can with hindsight be reinterpreted as a phase in a previous cycle of globalisation. However, economic history has overlooked the remarkable vitality of the small-scale transport sector. Focus on the small-scale sector highlights the inadequacies of familiar paradigms and suggests the need to reconceptualise long-term socioeconomic change. This analysis has important implications for responses to the current wave of globalisation and how they may be manifest in a more democratic post-Soeharto Indonesia.     

Understanding Firm, Physician and Consumer Choice Behavior in the Pharmaceutical Industry

This paper argues that the pharmaceutical industry represents an exciting opportunity to carry out academic research. The nature of the industry allows researchers to answer new questions, develop new methodologies for answering these questions as well as to apply existing methodology to new data. The paper opens with some industry background, then provides a brief overview of some important research areas and discusses the open questions in each area. Issues of data type and availability are also discussed. This paper is based on a session (with the same title and participants) that was part of the Sixth Invitational Choice Symposium hosted by the University of Colorado and held at Estes Park, Colorado during June 4–8, 2004.     

A unified approach to nonlinearity,structural change,and outliers

This paper demonstrates that the class of conditionally linear and Gaussian state-space models offers a general and convenient framework for simultaneously handling nonlinearity, structural change and outliers in time series. Many popular nonlinear time series models, including threshold, smooth transition and Markov-switching models, can be written in state-space form. It is then straightforward to add components that capture parameter instability and intervention effects. We advocate a Bayesian approach to estimation and inference, using an efficient implementation of Markov Chain Monte Carlo sampling schemes for such linear dynamic mixture models. The general modelling framework and the Bayesian methodology are illustrated by means of several examples. An application to quarterly industrial production growth rates for the G7 countries demonstrates the empirical usefulness of the approach.