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.

Here is my CV. Here is my Google Scholar page.

Work in Progress

2. Identification and Estimation of Finite Mixture in Dynamic Discrete Choice Models with Unobserved Heterogeneity (joint with Cheng Chou and Nan Liu, draft available soon)

1. Deep Learning for Individual Heterogeneity with Generated Regressors by Adversarial Training (joint with Yifei Ding)

Working Papers

3. Econometric Inference Using Hausman Instruments (joint with Jinyong Hahn, Zhipeng Liao and Nan Liu, under review) [supplemental appendix]

Abstract: We examine econometric inferential issues with Hausman instruments. The instrumental variable (IV) estimator based on Hausman instrument has a built-in correlation across observations, which may render the textbook-style standard error invalid. We develop a standard error that is robust to these problems. Clustered standard error is not always valid, but it can be a good pragmatic compromise to deal with the interlinkage problem if Hausman instrument is to be used in econometric models in the tradition of Berry, Levinsohn, and Pakes (1995).

2. Identification and Estimation of Nonstationary Dynamic Binary Choice Models (joint with Cheng Chou and Geert Ridder, (resubmission requested at Journal of Econometrics)

Abstract: In a dynamic binary choice model that allows for general forms of nonstationarity, we transform the identification of the flow utility parameters into the solution of a (linear) system of equations. The identification of the parameters, therefore, follows the usual argument for linear GMM. In particular, we show that the state transition distribution is not essential for the identification and estimation of the parameters. We propose a three-step conditional-choice-probability-based semiparametric estimator that bypasses estimation of and simulating from the state transition distribution. Simulation experiments show that our estimator gives comparable or better estimates than a competitor estimator, yet it requires fewer assumptions in certain scenarios, is substantially easier to implement, and is computationally much less demanding. The asymptotic distribution of the estimator is provided, and the sensitivity of the estimator to a key assumption is also examined.

1. Identification and Estimation of Nonparametric Hedonic Equilibrium Model with Unobserved Quality (resubmission requested at Journal of Econometrics)

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.