Nicholas M. Kiefer




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Research Summary


Professor Kiefer's interest in combining theoretical economics and statistics has led to structural empirical models in a number of fields.  His early work on program evaluation proposed methods of sorting out employment effects from wage effects of training in determining earnings.  This was followed in joint work with George Neumann by the first structural estimation of job search models of the labor market.  This was followed up by research on specification and estimation of equilibrium search models, in work with Neumann, B.J. Christensen and others. In finance, Kiefer has estimated models of market microstructure, with special emphasis on the roles of information and liquidity in determining price adjustments.  The PIN statistic invented in this work, an estimate of the probability of an informed trade, is now in wide use in empirical finance. This work was joint with David Easley and Maureen O'Hara.  Derivative pricing and the incorporation of options data in estimating financial models is the topic of joint work with Christensen. Theoretical work on the optimal accumulation and valuation of information (partially joint with Easley, Nyarko, and Bala) showed that full information accumulation is not always optimal and provided guidance for optimal experimentation. Current work in finance includes specification and evaluation of credit scoring models, estimation of default probabilities for financial institutions and portfolio risk measurement and management.

In econometrics, Kiefer's work has dealt with heterogeneity, in the form of regime shifts in switching regressions, individual effects in nonlinear models, and linear and nonlinear panel data models.  Most of the work is highly structural and likelihood based, although recent work with Christensen treats estimation of options models by simulation from the "risk-neutral measure," followed by method of moments.  Recent work with Christensen focuses on separate inference and the geometry of estimation.  Other recent work with Tim Vogelsang and Helle Bunzel focuses on robust testing in time series models, introducing a novel method of approximating the distributions of test statistics. Recent work with Hwansik Choi extends these results to nonnested testing.  Other work with Choi emphasizes the geometric approach to bias correction and high-order asymptotics.  Recent work on inference about small probabilities has been applied to default probability estimation and will see other banking applications.

Details on this and other work can be found by following the references in the CV. Also see Economic Modeling and Inference, with B.J. Christensen.

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