School of Business
rss twitter LinkedIn Facebook YouTube Blogs

Past Seminars in Fall 2008:

Title: The Role of Informational Spillovers on Competitive R&D Search

Speaker: Nektarios Oraiopoulos
College of Management
Georgia Institute of Technology


A number of studies on innovation and technological change show that scientific knowledge is rarely fully contractible, but instead disseminates through various communication channels. At an operational level, these knowledge spillovers translate as follows: R&D managers obtain information regarding the technological potential of different options that stem from past research accomplishments. A case study conducted by the authors in a leading research center shows the existence of such informational spillovers, and more importantly, their critical role in shaping firms' future R&D efforts. We develop a model that conceptualizes R&D as a process of exploration trials for new technological improvements that may emerge from the same or different scientific domains. Firms account for strategic intent when assessing the direction of their R&D efforts because they compete within similar markets. We find that R&D search choices are strongly path dependent. Future decisions rely on a threshold policy: technological breakthroughs prompt search within the same scientific domain, a herding-like behavior. Yet, moderate improvements may divert firms to explore new areas. We show that the managerial implications of such learning mechanisms on the R&D search path are not uniform. As past outcomes become more informative of the explored domain, they allow for faster learning, and exploitation of the same domain is more promising. On the contrary, an increased ability to learn from different scientific domains, due to strong similarities in their underlying knowledge base, renders diversification preferable.

Time: Wednesday, December 17th 11:45 am - 12:45 pm

Location: Funger Hall 520

Title: Bayesian Methods in Project Management

Speaker: Fabrizio Ruggeri


Different aspects of project management are illustrated. They are the results of research projects, still ongoing, and consulting activities which involved CNR-IMATI, Politecnico di Milano and Universidad Rey Juan Carlos de Madrid, and a leading Italian company. Major emphasis will be devoted to the bidding process, when a company is interested in estimating costs and benefits from taking part in a bid, finalised to the construction of an industrial plant. Three aspects will be considered: forecasts of costs due to construction and losses due to rare but disruptive events, and modelling of competitors' behaviour. Finally, we address the issue of execution of activities in due time, focusing on forecast of subcontractors' deliveries and critical chain and buffer management.

Time: Friday, December 12th 3:30 - 4:30 pm

Location: Duques Hall 453

Title: Improving Forecast Verification through the Incorporation of Baseline Distributions

Speaker: Victor Richmond R. Jose
The Fuqua School of Business
Duke University


The need for a quantitative measure of information - or more generally, a practical measure of the distance from one distribution to some other distribution - arises in many fields such as forecasting (where scoring rules are used to provide incentives for probability estimation), signal processing (where information gain is measured in physical units of relative entropy), decision analysis (where new information can lead to improved decisions), and finance (where investors optimize portfolios based on their private information and risk preferences). In this talk, we generalize the two most commonly used parametric families of scoring rules and demonstrate their relation to well-known generalized entropies and utility functions, shedding new light on the characteristics of alternative scoring rules as well as duality relationships between utility maximization and entropy minimization. The novel feature of these scoring rules is that they allow probability forecasts to be evaluated relative to a pre-specified, not-necessarily-uniform baseline distribution. Some interesting connections and extensions of these rules in other application domains are also presented.

Time: Thursday, December 11th 11:15 am - 12:15 pm

Location: Funger Hall 520

Title: Analysis of Multi-Factor Affine Yield Curve Models

Speaker: Siddhartha Chib
Harry C. Hartkopf Professor of Econometrics and Statistics
Olin Business School, Washington University in St. Louis


In finance and economics, there is a great deal of work on the theoretical modeling and statistical estimation of the yield curve (defined as the relation between -log(pt(t))/t and t, where pt(t) is the time price of the zero-coupon bond with payoff 1 at maturity date t + t). Of much current interest are models in which the bond prices are derived from a stochastic discount factor (SDF) approach that enforces an important no-arbitrage condition. The log of the SDF is assumed to be an affine function of latent and observed factors, where these factors are assumed to follow a stationary Markov process. In this paper we revisit the question of how such multi-factor affine models of the yield curve should be fit. Our discussion is from the Bayesian MCMC viewpoint, but our implementation of this viewpoint is different and novel. Key aspects of the inferential framework include (i) a prior on the parameters of the model that is motivated by economic considerations, in particular, those involving the slope of the implied yield curve; (ii) posterior simulation of the parameters in ways to improve the efficiency of the MCMC output, for example, through sampling of the parameters marginalized over the factors, and through tailoring of the proposal densities in the Metropolis-Hastings steps using information about the mode and curvature of the current target based on the output of a simulating annealing algorithm; and (iii) measures to mitigate numerical instabilities in the fitting through reparameterizations and square root filtering recursions.We apply the techniques to explain the monthly yields on nine US Treasuries (with maturities ranging from 1 to 120 months) over the period January 1986 to December 2005. The model contains three factors, one latent and two observed. We also consider the problem of predicting the nine yields for each month of 2006. We show that the (multi-step ahead) prediction regions properly bracket the actual yields in those months, thus highlighting the practical value of the fitted model..

Time: Friday, November 21st 10:45 - 11:45 am

Location: Duques Hall 554

Title: Separable but not equal: The location determinants of discrete services offshoring activities

Speaker: Eugene D. Hahn
Salisbury University


In this research, we explore the question of why firms offshore particular services to specific geographic locations. We draw on classic research related to the unique characteristics of services in trade and commerce, and more recent analyses of the transnational unbundling and spatial dispersion of business processes. We move beyond a simple assessment of the costsensitivity or relative sophistication of offshoring services and develop a typology emphasizing the degree to which offshoring services activities are interactive, repetitive, or innovative. We suggest the location of offshoring projects will depend on the particular mix of these attributes and test these hypotheses using a dataset of 595 export-oriented offshore services projects initiated from 2002-2005 by U.S. and U.K. company parents in 45 developed and developing countries, finding that offshore location choices greatly depend on these services characteristics. We draw implications for our findings with respect to international business theory, policy, and practice.

Time: Friday, November 14th 3:30 - 5:00 pm

Location: Funger Hall 520

Title: Information-Theoretic and Entropy Methods of Estimation

Speaker: Amos Golan
Department of Economics
American University


In this talk I will review the state of Information Theoretic and Entropy Methods in Econometrics. I will discuss the connecting theme among these methods and will provide a more detailed discussion of the sub-class of methods that treat the observed sample moments as stochastic. The resulting method uses minimal distributional assumptions, performs well (relative to current methods of estimation) and uses efficiently all the available information (hard and soft data). This method is computationally efficient. I will present the basic ideas using a number of empirical examples taken from economics, physics, image reconstruction and operation research. Studying these examples will provide a way for a synthesis of that class of models and connecting it to the more traditional methods of data analysis. I will conclude with some thoughts on potential future developments.

Time: Friday, October 3rd 11:00 am - 12:00 noon

Location: Funger Hall 520