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Associate Professor Youngki Shin


Youngki Shin has joined the Economics Discipline Group in July 2015. He has received his PhD in economics from the University of Rochester in 2007 and has held an academic position at the University of Western Ontario before joining UTS. 

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Associate Professor, Economics Discipline Group
Member, Korea-America Economic Association
Member, American Statistical Association
Member, Canadian Economics Association
Member, American Economic Association
Member, The Econometric Society
+61 2 9514 4021

Research Interests

Econometrics, Labour Economics, Statistics
Econometrics, Principles of Economics

Journal articles

Lee, S., Seo, M.H. & Shin, Y. 2016, 'The lasso for high dimensional regression with a possible change point', Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 78, no. 1, pp. 193-210.
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Jun, S.J., Lee, Y. & Shin, Y. 2016, 'Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach', Journal of Business and Economic Statistics, vol. 34, no. 2, pp. 302-311.
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© 2016 American Statistical Association. We propose the sharp identifiable bounds of the potential outcome distributions using panel data. We allow for the possibility that statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the sharp bounds. Our approach allows for dynamic treatment decisions, where the current treatment decisions may depend on the past treatments or the past observed outcomes. As an empirical illustration, we study the effect of smoking during pregnancy on infant birthweight. We find that for the group of switchers the infant birthweight of a smoking mother is first-order stochastically dominated by that of a nonsmoking mother.
Abrevaya, J. & Shin, Y. 2011, 'Rank estimation of partially linear index models', Econometrics Journal, vol. 14, no. 3, pp. 409-437.
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Summary We consider a generalized regression model with a partially linear index. The index contains an additive non-parametric component in addition to the standard linear component, and the model is characterized by an unknown monotone link function. We propose weighted rank estimation procedures for estimating (a) the coefficients for the linear component, (b) the non-parametric component (and its derivative) and (c) the average derivative for the non-parametric component. The method is applied to study the non-linear relationship between household income and children's cognitive development. © 2011 The Author(s). The Econometrics Journal © 2011 Royal Economic Society.
Lee, S., Seo, M.H. & Shin, Y. 2011, 'Testing for threshold effects in regression models', Journal of the American Statistical Association, vol. 106, no. 493, pp. 220-231.
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In this article, we develop a general method for testing threshold effects in regression models, using sup-likelihood-ratio (LR)-type statistics. Although the sup-LR-type test statistic has been considered in the literature, our method for establishing the asymptotic null distribution is new and nonstandard. The standard approach in the literature for obtaining the asymptotic null distribution requires that there exist a certain quadratic approximation to the objective function. The article provides an alternative, novel method that can be used to establish the asymptotic null distribution, even when the usual quadratic approximation is intractable. We illustrate the usefulness of our approach in the examples of the maximum score estimation, maximum likelihood estimation, quantile regression, and maximum rank correlation estimation. We establish consistency and local power properties of the test. We provide some simulation results and also an empirical application to tipping in racial segregation. This article has supplementary materials online. © 2011 American Statistical Association.
Khan, S., Shin, Y. & Tamer, E. 2011, 'Heteroscedastic transformation models with covariate dependent censoring', Journal of Business and Economic Statistics, vol. 29, no. 1, pp. 40-48.
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In this article we propose an inferential procedure for transformation models with conditional heteroscedasticity in the error terms. The proposed method is robust to covariate dependent censoring of arbitrary form.We provide sufficient conditions for point identification.We then propose an estimator and show that it is n-consistent and asymptotically normal. We conduct a simulation study that reveals adequate finite sample performance. We also use the estimator in an empirical illustration of export duration, where we find advantages of the proposed method over existing ones. © 2011 American Statistical Association.
Shin, Y. 2010, 'Local rank estimation of transformation models with functional coefficients', Econometric Theory, vol. 26, no. 6, pp. 1807-1819.
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This paper considers a nonparametric functional coefficient model with an unknown link function. The model gives flexibility to the standard interaction-variable model by allowing an arbitrary functional form of heterogeneous marginal effects. A local rank estimation procedure is proposed for the functional coefficients along with its asymptotic property. © Cambridge University Press 2010.
Shin, Y. 2009, 'Misspecified markov switching model', Economics Bulletin, vol. 29, no. 2, pp. 957-963.
I characterize the local power of an optimal test for a Markov Switching model under generalized alternatives. The result shows that the test still has power for the model with endogenous stochastic parameters unless they are orthogonal to the score functions.
Shin, Y. 2009, 'Length-bias Correction in Transformation Models with Supplementary Data', Econometric Reviews, vol. 28, no. 6, pp. 658-681.
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Shin, Y. 2008, 'Rank estimation of monotone hazard models', Economics Letters, vol. 100, no. 1, pp. 80-82.
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I consider a class of hazard models that satisfy a flexible monotone restriction. A rank estimation procedure can be applied to this class. The result sheds light on the extension of rank estimation methods to hazard models with time-varying covariates. © 2007 Elsevier B.V. All rights reserved.
Shin, Y. 2008, 'Semiparametric estimation of the Box-Cox transformation model', Econometrics Journal, vol. 11, no. 3, pp. 517-537.
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In this paper, I propose a semiparametric estimation procedure for the Box-Cox transformation model. I show a global identification result under mild conditions that allow conditional heteroskedastic error terms. The proposed estimator minimizes a second order U-process and does not require any user-chosen values such as a smoothing parameter that sometimes induces unstable inference result. With a slight modification, it can also be applied to random censoring which depends on covariates in an arbitrary way. The estimator converges to an asymptotic normal distribution at the rate of and Monte Carlo experiments show adequate finite sample performance. © The Author(s). Journal compilation © 2008 Royal Economic Society.
Selected Peer-Assessed Projects