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Published online by Cambridge University Press: 06 December 2006
Much of the credit for the outpouring of research on nonlinear models in the 1970s—particularly limited dependent variable models—should go to Takeshi Amemiya. His classic 1973 Econometrica article on estimation of the parameters of the Tobit model [11] set a new standard for mathematical rigor in theoretical econometrics. During his career, which spans nearly four decades, Takeshi's research contributions to econometrics have touched on most of the central models for empirical economics, including linear and nonlinear simultaneous equations models, distributed lag models, heteroskedastic and random coefficient models, qualitative response models, censored and truncated regression and selection models, transformed regression models, choice-based sampling models, and duration models. His 1985 text, Advanced Econometrics [44], became the standard reference for second-year graduate microeconometrics courses at the leading graduate programs in economics.
Takeshi's research accomplishments have been accompanied by numerous professional honors. He is a Fellow of the Econometric Society, the American Statistical Association, and the American Academy of Arts and Sciences and was awarded Guggenheim and Humboldt Fellowships. Takeshi also has a distinguished record of professional service; he served briefly as a co-editor of Econometrica and has been a co-editor of the Journal of Econometrics since 1982.
This interview was conducted in Takeshi Amemiya's office at Stanford University in March 2004. In recent years his interests have turned to comparative study of Greek and Japanese culture and mythology—his scholarship includes a Japanese translation of an English research monograph on Aristotle's Ethics [58]—and the discussion starts with this topic before turning to his unusual education, career as an econometric theorist, and variety of outside interests. His remarks reveal both his careful attention to detail and the dry humor that his colleagues and former students (like me) savor.