Department of Economics
2001 Sheridan Road
Evanston, IL 60208
Ph.D., Economics, Northwestern University, 2017 (expected)
M.A., Economics, Northwestern University, 2016 (expected)
M.Sc., Econometrics and Mathematical Economics (Research track), London School of Economics, UK, 2011
M.A., Economics, New Economic School, Moscow, Russia, 2010 (GPA 4.7)
M.Sc., Applied Mathematics and Physics, Moscow Institute of Physics and Technology, Russia, 2010 (summa cum laude)
B.Sc., Applied Mathematics and Physics, Moscow Institute of Physics and Technology, Russia, 2008 (summa cum laude)
Primary Fields of Specialization
Job Market Paper
“Penalized maximum likelihood estimation of finite mixture models”
Economic models often resort to finite mixtures to accommodate unobserved heterogeneity. In practice, the number of components in the mixture is rarely known. If too many components are included in the estimation, then the parameters of the estimated model are not point-identified and lie on the boundary of the parameter space. This invalidates the classic results on maximum likelihood estimation. Nonetheless, the parsimonious model, which corresponds to a particular subset of the identified set, can be point-identified. I propose a method to estimate finite mixture models with an unknown number of components by maximizing a penalized likelihood function, where the penalty is applied to the mixing coefficients. The resulting Order-Selection-Consistent Estimator (OSCE) consistently estimates the true number of components in the mixture, and achieves the oracle efficiency for the parameters of the parsimonious model. This paper extends the literature on penalized estimation to the case of non-identified model parameters. Further, numerical simulations illustrate the performance of the proposed method in practice. Finally, the method is applied to the experimental data from Cornand and Heinemann (2014) to determine the composition of subjects’ types associated with their level of rationality in a coordination game.
Other Research Papers
“The Ambiguity of Earnings Announcements”, with Davide Cianciaruso and Ivan Marinovic, 2016 (submitted)
We study the consequences of misreporting in settings where ambiguity-averse investors face uncertainty about two aspects of the firm: productivity and reliability of the information system. We show that these two sources of uncertainty distort the firm’s investment choice in distinct ways, leading to over-investment by large firms (which signal productivity) and to under-investment by small firms (which signal reliability).
Our analysis suggests that uncertainty regarding the reliability of financial statements affects both the level of the market-to-book ratio and its association with firm size. In addition, we show that, under plausible circumstances, reductions in uncertainty can be detrimental to social welfare: lower information asymmetry about reliability always encourages more aggressive misreporting and boosts investment, thereby exacerbating the possible over-investment problem of some firms.