A Conversation with James O. Ramsay

Jim Ramsay was born on September 5, 1942, in Prince George, British Columbia. He pursued undergraduate studies at the University of Alberta, where he completed a BEd in 1964 with a major in English and a minor in mathematics. He then specialized in statistics and psychometry, earning a PhD in psychology from Princeton University in 1966. After holding a temporary lectureship in the Department of Psychology at University College London for one year, he joined the Department of Psychology at McGill University, where he rose through the academic ranks. He was chair of his department from 1986 to 1989 and spent sabbatical leaves in Cambridge, Grenoble, and Toulouse. He was named professor emeritus upon his retirement in 2007. Jim is the author of four influential books and over 100 peer‐reviewed articles in statistical and psychometric journals. He developed much of the statistical theory behind multidimensional scaling and is widely recognized as the founder of functional data analysis. Three of his papers were read to the Royal Statistical Society, and another won The Canadian Journal of Statistics 2000 Best Paper Award. The Statistical Society of Canada (SSC) awarded him a Gold Medal for research in 1998 and an honorary membership in 2012. Jim was president of the Psychometric Society in 1981–82 and president of the SSC in 2002–03. The following conversation took place at Jim's home in Ottawa, Ontario, on March 14 and April 4, 2012.     

Institutional dimensions underlying public trust in information on technological risk

This paper examines the determinants of trust in public information on technological risk in a petrochemical complex located in Tarragona (Spain). Data from focus groups (eight) and a questionnaire survey (N = 400) are drawn together to analyse how two local communities exposed to major chemical hazards perceive information on risk and its sources. Results show how trust relies on two main factors, namely expertise and trustworthiness, that are significantly influenced by a third one, antagonism. Results also illustrate the relevance of the institutional context when understanding how communities give meaning to the available information on risk.     

The effect of capital requirement regulation on the transmission of monetary policy: evidence from Austria

This paper analyzes the role of banks’ regulatory capitalization in the transmission of monetary policy. We use a confidential dataset for Austrian banks spanning from the first quarter of 1997 to the fourth quarter of 2003. We find evidence that Austrian banks react in an asymmetric way to monetary policy depending on their regulatory excess capitalization, i.e. low capitalized banks react more restrictively to a monetary tightening than their highly capitalized peers.

Gambling in contests

This paper presents a strategic model of risk-taking behavior in contests. Formally, we analyze an n-player winner-take-all contest in which each player decides when to stop a privately observed Brownian motion with drift. A player whose process reaches zero has to stop. The player with the highest stopping point wins. Unlike the explicit cost for a higher stopping time in a war of attrition, here, higher stopping times are riskier, because players can go bankrupt. We derive a closed-form solution of a Nash equilibrium outcome. In equilibrium, highest expected losses occur at an intermediate negative value of the drift.     

The role of venture capitalist to enhance the growth of Spanish and Italian university spin-offs

The establishment of spin-offs to commercialise university knowledge/technology is a potential mechanism to promote economic and innovative development. Nevertheless, University Spin-Offs (USOs) are usually resource-constrained, especially in obtaining funding, limiting their growth. Venture Capital (VC) investors play an important role in the financing and the improvement of their managerial skills, which are critical for firm growth. This paper aims to explore both the effect of VC partners on the USOs’ growth and the cross-national differences in the role played by them. To study both issues, we empirically analysed 516 Spanish and 904 Italian USOs created by 50 Spanish and 57 Italian universities, respectively, and observed them between 2005 and 2013. The results showed different effects in the Spanish and Italian cases. While in Spain the presence of VC partners positively affects the USOs’ growth, in Italy there is not a significant effect. This evidence calls for systematic policies by public administrations and universities to foster USO growth.     

CHOOSING THE VARIABLES TO ESTIMATE SINGULAR DSGE MODELS

We propose two methods to choose the variables to be used in the estimation of the structural parameters of a singular DSGE model. The first selects the vector of observables that optimizes parameter identification; the second selects the vector that minimizes the informational discrepancy between the singular and non‐singular model. An application to a standard model is discussed and the estimation properties of different setups compared. Practical suggestions for applied researchers are provided. Copyright © 2014 John Wiley & Sons, Ltd.     

An efficient nonparametric estimator for models with nonlinear dependence

We provide a convenient econometric framework for the analysis of nonlinear dependence in financial applications. We introduce models with constrained nonparametric dependence, which specify the conditional distribution or the copula in terms of a one-dimensional functional parameter. Our approach is intermediate between standard parametric specifications (which are in general too restrictive) and the fully unrestricted approach (which suffers from the curse of dimensionality). We introduce a nonparametric estimator defined by minimizing a chi-square distance between the constrained densities in the family and an unconstrained kernel estimator of the density. We derive the nonparametric efficiency bound for linear forms and show that the minimum chi-square estimator is nonparametrically efficient for linear forms.     

The topography of metropolitan employment: Identifying centers of employment in a polycentric urban area

While it is well recognized that US metropolitan areas are polycentric, there is little consensus as to the appropriate method for identifying concentrations of employment within them. Existing methods suffer from strong assumptions about parametric form, misspecification, or reliance on local knowledge to calibrate model parameters. This paper introduces a new nonparametric method for identifying subcenters. Results indicate that this, more flexible, nonparametric approach yields greater accuracy with regard to both urban and suburban centers compared with other approaches. This approach should provide better data for the numerous topics that depend on the spatial accounting of employment within metropolitan areas.