NYU-CRATE is extremely happy to welcome the newest member of our research staff, Professor Timothy Christensen. Professor Christensen recently completed his PhD in Economics from Yale University in 2014, and has now joined the faculty of the NYU Economics Department. He is an econometric theorist specializing in nonparametric methods and financial econometrics. His current research interests include developing econometric methods to help analyze asset pricing models, as well as optimal and adaptive estimation of nonparametric models. He will strengthen and broaden econometrics at NYU Economics and within our center, we look forward to having him as part of our group.
NYU-CRATE is delighted to welcome Xiaohong Chen as an NYU-CRATE Research Affiliate. Professor Chen is the Malcolm K. Brachman Professor of Economics at Yale University. She is a leading econometric theorist, and has done seminal work on semi/nonparametric estimation and inference methods especially in the context of sieve methodology. She will be a regular visitor to our center, and we look forward to her enriching the intellectual environment for econometrics at NYU.
NYU-CRATE is delighted to welcome Azeem Shaikh as an NYU-CRATE Research Affiliate. Professor Shaikh is a Professor of Economics at the University of Chicago. He is an econometric theorist specializing in inference for nonstandard problems in econometrics and statistics, and is most noted for his seminal work on inference for partially identified models and on multiple hypothesis testing. He will be a regular visitor to our center, and we look forward to him enriching the intellectual environment for econometrics at NYU.
NYU-CRATE is excited to announce that Professor Emmanuel Guerre will be visiting NYU-CRATE for the 2014-15 academic year. Dr. Guerre is a Professor of Economics at the School of Economics and Finance at Queen Mary, University of London, with interests in both theoretical and applied econometrics we well as empirical industrial organization. Emmanuel’s research includes work on nonparametric identification and inference for auctions and nonparametric inference. His recent work deals with a new quantile methodology for conditional dependence analysis with applications to first-price auction signal models.
NYU-CRATE is excited to announce that Dr. Stepana Lazarova will be visiting NYU-CRATE for the 2014-15 academic year. Dr. Lazarova is a Senior Lecturer at the School of Economics and Finance at Queen Mary, University of London, specializing in time-series and panel data econometrics. In her research, she has investigated time series with long memory in the presence of structural breaks; she has examined how the spatial proximity of cities influences the evolution of city price indexes; and she has also developed nonparametric methods that can be employed for the detection of correlation in time series.
Professor Montiel Olea’s paper, `Axiomatization and measurement of quasi-hyperbolic discounting’, is now forthcoming at the Quarterly Journal of Economics. This article combines axiomatic decision theory and econometric methodology to propose a novel way to measure the discount factors of a quasi-hyperbolic model of intertemporal choice. The first part of the paper presents an axiomatic characterization of quasi-hyperbolic discounting and a more general class of semi-hyperbolic preferences. The axiomatization leads naturally to an experimental design that disentangles discounting from the elasticity of intertemporal substitution. The second part of the paper concerns the use of data generated by their proposed experimental design to conduct inference about the distribution of discounting parameters in a population of agents. The authors show that their questionnaire yields bounds on individual preference parameters, and consequently, on the distribution of preference parameters over the population. They use the partial identification approach to estimate bounds for the distributions of discount factors in the subject pool. Consistent with previous studies, they find evidence for both present and future bias.