Ruoyao Shi (史若瑶)
I am an assistant professor at Department of Economics, University of California, Riverside. My research areas are econometrics methods and applied microeconomics.
6. Influence Function of Semiparametric Two-step Estimator with Estimated Control Variables (joint with Jinyong Hahn, Zhipeng Liao and Geert Ridder, September 2020, draft available soon)
Abstract: This paper studies semiparametric two-step estimator with a control variable estimated in a first-step parametric or nonparametric model. We provide the explicit influence function of the two-step estimator under an index restriction which is imposed directly on the unknown control variable. The index restriction is weaker than the commonly used identification conditions in the literature, which are imposed on all exogenous variables. An extra term shows up in the influence function of the semiparametric two-step estimator under the weaker identification condition. We illustrate our influence function formula in a mean regression example, a quantile regression example and a sample selection example where the control variable approach is applied for identification and consistent estimation of structural parameters.
5. An Averaging Estimator for Two Step M Estimation in Semiparametric Models (March 2020, new draft available soon)
Abstract: In this paper, we study the two step M estimation of a finite dimensional parameter which depends on a first step estimation of a potentially infinite dimensional nuisance parameter. We present an averaging estimator that combines a semiparametric estimator based on nonparametric first step and a parametric estimator which imposes parametric restrictions on the first step. The averaging weight is the sample analog of an infeasible optimal weight that minimizes quadratic risk functions. This averaging estimator strikes a balance between the robust semiparametric estimator and the efficient parametric estimator, as we show that the averaging estimator uniformly dominates the semiparametric estimator in terms of asymptotic quadratic risk regardless of whether the first step parametric restrictions hold or not. In particular, we prove that under certain sufficient conditions, the asymptotic lower bound of the truncated quadratic risk differences between the averaging estimator and the semiparametric estimator is strictly less than zero under a class of data generating processes that includes both correct specification and misspecification of the first step parametric restrictions, and the asymptotic upper bound is weakly less than zero. We illustrate our estimator in a variety of applications using simulations and real data.
Abstract: We document that the Gibbons, Ross, and Shanken (1989) F-test of mean-variance efficiency of asset returns is most likely frequently calculated incorrectly, possibly due to an ambiguity in the original statement of the test. We derive the correct formula for the test statistic for the general case of K factors and N test assets, then highlight the error in common applications. Simulations show that the error leads to over-rejection of the null of market efficiency, and the use of the F-test for ranking competing models is also threatened if the test is calculated incorrectly. We also provide some empirical examples.
3. Utilizing Two Types of Survey Data to Enhance the Accuracy of Labor Supply Elasticity Estimation (joint with Cheng Chou, July 2020)
Abstract: We argue that despite its nonclassical measurement errors, the hours worked in the Current Population Survey (CPS) can still be utilized to enhance the overall accuracy of the estimator of the labor supply parameters based on the American Time Use Survey (ATUS), if done properly. We propose such an estimator that is a weighted average between the two stage least squares estimator based on the CPS and a non-standard estimator based on the ATUS.
2. What Time Use Surveys Can (And Cannot) Tell Us About Labor Supply (joint with Cheng Chou, October 2020, Revised & Resubmitted to Journal of Applied Econometrics)
Abstract: The American Time Use Survey (ATUS) accurately measures hours worked on a single day. The analysis in Frazis and Stewart (2012) implies that weekly labor supply regression can be estimated using the ATUS daily hours, despite certain impossibility results due to the time specific feature of the ATUS. In this paper, we propose several new estimators of elasticities of weekly labor supply. We recommend the impute estimator, a simple modification of the standard two stage least squares (2SLS) estimator, that imputes the dependent variable using daily subsamples, based on our careful investigation of asymptotic and finite sample properties of the estimators under the potential outcome framework. We apply the impute estimator to the ATUS and find substantially different elasticity estimates from the CPS, especially for married women.
1. Identification and Estimation of Nonparametric Hedonic Equilibrium Model with Unobserved Quality (November 2019, under review)
Abstract: This paper studies a nonparametric hedonic equilibrium model in which certain product characteristics are unobserved by researchers. Unlike most previously studied hedonic models, the observed and unobserved agent heterogeneities both enter the structural functions nonparametrically. Prices are endogenously determined in the equilibrium. Using both within market and cross market price variations, I show the nonparametric identification of all the structural functions of the model up to normalization. In particular, the unobserved product quality function is identified if the relative prices of the agent characteristics differ in at least two markets. Following the constructive identification strategy, I provide easy-to-implement series minimum distance estimators of the structural functions and derive their uniform rates of convergence. To demonstrate the estimation procedure, I estimate the unobserved efficiency of American full-time workers as a function of age and unobserved ability.
2. Xu Cheng, Zhipeng Liao and Ruoyao Shi (2019), "On Uniform Asymptotic Risk of Averaging GMM Estimators." Quantitative Economics, 10(3), 931-979.
- Econometric Methods I (Fall 2019 & Fall 2018)
- Topics in Advanced Econometrics (Fall 2019 & Fall 2018)
- Empirical Methods in Applied Microeconomics (Fall 2018 & Fall 2017)
- Introductory Econometrics (Spring 2020, Fall 2019, Spring 2019, Winter 2018, & Fall 2017)
Awards and Fellowships
As Assistant Professor
- Hellman Fellowship (Hellman Foundation, 2020)
- CHASS Proposal Incentive Award (UCR, 2019)
- Regents' Faculty Fellowship for Assistant Professor (UCR, 2019)
- Dissertation Year Fellowship (UCLA, 2016)
- Edwin W. Pauley Fellowship (UCLA, 2011)
- Distinguished Graduate Student (Peking University, 2011)
- Kwang-Hua Graduate Research Award (Peking University, 2009)
- Graduate Fellowships (Peking University, 2008-2010)
- Distinguished Undergraduate Student (Peking University, 2008)