Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models (with R. Liesenfeld) is now available.
Professor Richard presents his talk, "An Integrated Treatment of Monte Carlo Numerical Integration Techniques" on at the SCE 11th International Conference, June 2005.
The GDP Forecast Model at the University of Pittsburgh is devoted to reporting forecasts of GDP growth, and characterizations of the current state of the macroeconomy.
Jean-François Richard's research largely aims at developing operational inference procedures for complex empirical problems. Research interests can be loosely grouped into five main categories:
Bayesian Methods, with an emphasis on Simultaneous Equation Models, Errors-in-Variables models, models with autoregressive errors and inference on exogeneity.
Numerical integration methods with an emphasis on poly-t densities and, more recently, the development of Monte Carlo Efficient Importance Sampling methods for the numerical integration of very-high dimensional latent processes (stochastic volatility, unobserved heterogeneity in panels).
Econometric modeling of time series where Richard contributed to the development of important concepts such as exogeneity. With David Hendry, Richard developed a set of internally consistent criteria for the validation of econometric time series models.
Auction Theory, where Richard contributed to the analysis of collusive behavior in procurement processes, the development of generic estimation techniques for empirical game theoretic model and of exact as well as approximate numerical techniques for solving analytically intractable games such as asymmetric fixed price auctions.
Last but not least, Richard has conducted a number of empirical applications using some of those above developments in a variety of areas such as money demand, Allais paradox, procurements (DOD, U.S. Forest Service, school milk programs), stochastic volatility, winner's curse, legal systems and economic development, spatial taxation models and GDP forecasting.
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