A. Ronald Gallant

Ron Gallant is Liberal Arts Professor of Economics at The Pennsylvania State University.

Prior to joining the Penn State faculty he was Hanes Corporation Foundation Professor of Business Administration, Fuqua School of Business, Duke University, with secondary appointment in the Department of Economics, Duke University, and Distinguished Scientist in Residence, Department of Economics, New York University.

Before joining the Duke faculty, he was Henry A. Latane Distinguished Professor of Economics at the University of North Carolina at Chapel Hill. He retains emeritus status at UNC.

Previous to UNC he was, successively, Assistant, Associate, Full, and Drexel Professor of Statistics and Economics at North Carolina State University.

Gallant has held visiting positions at the École Polytechnique, the University of Chicago, and Northwestern University.

He received his A.B. in mathematics from San Diego State University, his M.B.A. in marketing from the University of California at Los Angeles, and his Ph.D. in statistics from Iowa State University. He is a Fellow of both the Econometric Society and the American Statistical Association. He has served on the Board of Directors of the National Bureau of Economic Research, the Board of Directors of the American Statistical Association, and on the Board of Trustees of the National Institute of Statistical Sciences. He is past co-editor of the Journal of Econometrics and past editor of The Journal of Business and Economic Statistics.

Gallant is interested in fitting models from the sciences to data for the purpose of statistical inference. Typically these models will involve a nonlinear parametric component that describes features of the model where the underlying scientific theory is explicit and a nonparametric component that accounts for features where the scientific theory is vague. Appropriate statistical methods for these problems are usually computationally intensive. Methodological interests are in developing statistical methods and numerical algorithms for fitting these models. Theoretical interests are in deriving the statistical properties of proposed methods, particularly the asymptotic properties of estimators of functionals of the nonparametric component. Applied interests are primarily within economics. The most recent application area is estimating dynamic stochastic general equilibrium models that have unobserved variables without having to solve the model.