Nail Kashaev

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I am an Economics Ph.D. candidate from the Pennsylvania State University specializing in Econometric Theory, Applied Econometrics and IO. I will be available for interviews at the 2016 ASSA meeting in San Francisco.

Testing for Nash behavior in entry games with complete information

(Job Market Paper)

Nash equilibrium (NE) is a leading solution concept in the empirical analysis of entry games. The NE assumption is crucial, not just for estimation, but also for the validity of counterfactual exercises and policy implications. I propose a computationally simple sieve likelihood ratio type procedure to test the NE assumption in a complete information entry game with second-order rational players. The method is robust to partial identification and allows for nonparametric selection of equilibria.


Identification of solution concepts for discrete semi-parametric games with complete information

(Joint work with Bruno Salcedo )

Empirical analysis of discrete games relies on behavioral assumptions that are crucial not just for estimation, but also for the validity of counterfactual analyses and policy implications. We find conditions to identify whether actual behavior satisfies these assumptions. Our results allow to identify whether and how often firms in an entry game play Nash equilibria, whether they enter simultaneously or sequentially, and whether their profit functions are private information or common knowledge.


Parametric estimation of binary models with contaminated controls

I propose a generalized method of moments type procedure to estimate parametric binary choice models when the researcher only observes the data with a particular response and has some information about the distribution of the covariates. An example might be where the firm observes characteristics of its own customers only and knows something about the distribution of the whole population of customers. This auxiliary information comes in the form of moments. I present an application based on the data on police-reported car accidents in Seattle. Publicly available information on the distribution of drivers' characteristics in Seattle allows me to estimate the probability of a two-car collision.


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Department of Economics
Pennsylvania State University
303 Kern Building
University Park, PA

Phone: +1 (814) 954-2885