Ivan Korolev
Job Market Candidate

Stanford University
Department of Economics
579 Serra Mall
Stanford, CA 94305
408-707-4005
ikorolev@stanford.edu

In August 2018, I will join the Department of Economics at Binghamton University as an Assistant Professor. My new website is located here.

Curriculum Vitae

Fields:
Econometrics (Primary), Industrial Organization, Applied Microeconomics

Expected Graduation Date:
June, 2018

Thesis Committee:
Frank Wolak (Primary):
wolak@stanford.edu

Han Hong:
doubleh@stanford.edu

Peter Reiss:
preiss@stanford.edu

Job Market Paper

Consistent Lagrange Multiplier Type Specification Tests for Semiparametric Models

This paper considers specification testing in semiparametric econometric models. It develops a consistent series-based specification test for semiparametric conditional mean models against nonparametric alternatives. Consistency is achieved by turning a conditional moment restriction into a growing number of unconditional moment restrictions using series methods. The test is simple to implement because it requires estimating only the restricted semiparametric model and because the asymptotic distribution of the test statistic is pivotal. The use of series methods in estimation of the null semiparamertic model allows me to account for the estimation variance and obtain refined asymptotic results. The test remains valid even if other semiparametric methods are used to estimate the null model as long as they achieve suitable convergence rates. This includes popular kernel estimators for single index or partially linear models. The test demonstrates good size and power properties in simulations. To illustrate the use of my test, I apply it to one of the semiparametric gasoline demand specifications from Yatchew and No (2001) and find no evidence against it.

Working Papers

Evaluating Russian Economic Growth without the Revolution of 1917

This paper uses modern econometric techniques, such as the lasso and the synthetic control method, to construct the counterfactual GDP per capita series for Russia for 1917-1940. The goal of this paper is twofold: first, to predict how the Russian economy might have developed without the Revolution; second, to evaluate and compare various econometric methods for computing the counterfactual GDP per capita series. The counterfactuals based on the preferred method, the synthetic control, suggest that without the Revolution Russia might have grown at about 1.6% a year in 1917-1940.

Research in Progress

Consistent Series-Based Specification Tests for Semiparametric Instrumental Variables Models

This paper considers specification testing in semiparametric instrumental variables econometric models, in which endogenous variables enter parametrically. It develops a consistent Conditional Moment type specification test for these models. Consistency is achieved by turning a conditional moment restriction into a growing number of unconditional moment restrictions using series methods. The test is simple to implement because it requires estimating only the restricted semiparametric instrumental variables model and because the asymptotic distribution of the test statistic is pivotal. The test demonstrates good size and power properties in simulations.

Varying Coefficient Models with Data Combination

Three issues arise frequently in empirical research in economics. First, researchers often want to find functional forms that are flexible enough yet satisfy restrictions imposed by economic theory. Second, individual heterogeneity, observed or unobserved, often plays an important role in various economic settings, and applied economists need to account for it properly. Third, in many cases, researchers do not observe all relevant variables in a single dataset and need to combine information from multiple sources. This paper addresses these challenges by considering identification and estimation in varying coefficient models with data combination. These models maintain a theoretically derived functional form at the individual level but allow the parameters to depend on individual characteristics in a nonparametric way. I consider the case when these individual characteristics are unobserved in the primary dataset, but an auxiliary dataset with some information about them is available. I provide conditions for identification and develop a two-step series estimation method that allows the researcher to recover the unknown coefficient functions. The proposed method performs well in simulations.