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.

Working Papers

5. The Influence Function of Semiparametric Two-step Estimators with Estimated Control Variables (joint with Jinyong Hahn, Zhipeng Liao and Geert Ridder, July 2022, under review)

Abstract: This paper studies semiparametric two-step estimators 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.

4. Test of Neglected Heterogeneity in Dyadic Models (joint with Jinyong Hahn and Hyungsik Roger Moon, February 2022, under review; an earlier version of this paper was circulated under the title "Breusch and Pagan's (1980) Test Revisited")

Abstract: We develop a Lagrange Multiplier (LM) test of neglected heterogeneity in dyadic models. The test statistic is derived by modifying Breusch and Pagan (1980)'s test. We establish the asymptotic distribution of the test statistic under the null using a novel martingale construction. We also consider the power of the LM test in generic panel models. Even though the test is motivated by random effects, we show that it has a power for detecting fixed effects as well. Finally, we examine how the estimation noise of the maximum likelihood estimator affects the asymptotic distribution of the test under the null, and show that such a noise may be ignored in large samples.

3. An Averaging Estimator for Two Step M Estimation in Semiparametric Models (June 2022, R&R at Econometric Theory)

Abstract: In a two step extremum estimation (M estimation) framework with a finite dimensional parameter of interest and a potentially infinite dimensional first step nuisance parameter, I propose 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 an easy-to-compute sample analog of an infeasible optimal weight that minimizes the asymptotic quadratic risk. I show that under Stein-type conditions, the asymptotic lower bound of the truncated quadratic risk difference between the averaging estimator and the semiparametric estimator is strictly less than zero for a class of data generating processes (DGPs) that includes both correct specification and varied degrees of misspecification of the parametric restrictions, and the asymptotic upper bound is weakly less than zero. The averaging estimator, along with an easy-to-implement inference method, is demonstrated in an example.

2. Testing and Ranking of Asset Pricing Models Using the GRS Statistic (joint with Mark Kamstra, August 2021, R&R at Journal of Banking & Finance) [R code]

Abstract: We clear up an ambiguity in Gibbons, Ross and Shanken (1989, GRS hereafter) by providing the correct formula of the GRS test statistic and proving its exact F distribution in the general multiple portfolio case. We generalize the Sharpe ratio based interpretation of the GRS test to the multiple portfolio case, which we argue paradoxically makes experts in asset pricing studies more susceptible to an incorrect formula. We theoretically and empirically illustrate the consequences of using the incorrect formula -- over-rejecting and mis-ranking asset pricing models.

1. Identification and Estimation of Nonparametric Hedonic Equilibrium Model with Unobserved Quality (December 2020, under review, new draft available soon)

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.