@article{li_2024, title={Nonparametric Identification and Inference of First-Price Auctions with Heterogeneous Bidders}, ISSN={["1537-2707"]}, DOI={10.1080/07350015.2023.2299432}, abstractNote={In the auction literature, it is well established that bidders’ asymmetry plays an important role in determining auction revenues. In this article, we propose a nonparametric methodology to analyze first-price auctions with two popularly adopted asymmetries: heterogeneous risk preferences and asymmetric value distributions. We find that the two competing models provide distinct implications for the bid distributions conditional on heterogeneity. By modeling bidders’ asymmetry as unobserved heterogeneity, we show that the conditional bid distributions are identified nonparametrically. These results enable researchers to test the two competing models. In an application using the US Forest Service timber auction data, we find that the data supports the model with heterogeneity in risk preference.}, journal={JOURNAL OF BUSINESS & ECONOMIC STATISTICS}, author={Li, Zheng}, year={2024}, month={Jan} }
 @article{li_li_hsiao_2023, title={Assessing the impacts of pandemic and the increase in minimum down payment rate on Shanghai housing prices}, ISSN={["1435-8921"]}, DOI={10.1007/s00181-023-02414-w}, abstractNote={In 2016, the city of Shanghai increased the minimum down payment rate requirement for purchasing various types of properties. We study the treatment effect of this major policy change on Shanghai's housing market by employing panel data from March 2009 to December 2021. Since the observed data are either in the form of no treatment or under the treatment but before and after the outbreak of COVID-19, we use the panel data approach suggested by Hsiao et al. (J Appl Econ, 27(5):705-740, 2012) to estimate the treatment effects and a time-series approach to disentangle the treatment effects and the effects of the pandemic. The results suggest that the average treatment effect on the housing price index of Shanghai over 36 months after the treatment is -8.17%. For time periods after the outbreak of the pandemic, we find no significant impact of the pandemic on the real estate price indices between 2020 and 2021.}, journal={EMPIRICAL ECONOMICS}, author={Li, Hongjun and Li, Zheng and Hsiao, Cheng}, year={2023}, month={Apr} }
 @article{li_rejesus_zheng_2021, title={Nonparametric Estimation and Inference of Production RiskJEL codes}, volume={103}, ISSN={["1467-8276"]}, DOI={10.1111/ajae.12154}, abstractNote={This paper proposes a nonparametric approach for estimation of stochastic production functions with categorical and continuous variables, and then develops procedures that allow for inference on production risk. The estimation is based on the kernel method and the inference is based on a bootstrapping approach. We establish the asymptotic properties of our proposed estimator. Monte Carlo simulation results suggest that our proposed nonparametric procedure is more robust and outperforms other existing parametric and nonparametric methods. In addition, we empirically illustrate the proposed nonparametric approach using long‐run corn production data from university field trials in Wisconsin that examines the performance of genetically modified corn varieties. Specifically, the proposed nonparametric procedure is used to empirically examine the production risk effects of categorical genetically modified variety variables and a continuous planting density variable.}, number={5}, journal={AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS}, author={Li, Zheng and Rejesus, Roderick M. and Zheng, Xiaoyong}, year={2021}, month={Oct}, pages={1857–1877} }
 @article{li_li_li_2021, title={Nonparametric Quantile Regression Estimation With Mixed Discrete and Continuous Data}, volume={39}, ISSN={["1537-2707"]}, DOI={10.1080/07350015.2020.1730856}, abstractNote={Abstract In this article, we investigate the problem of nonparametrically estimating a conditional quantile function with mixed discrete and continuous covariates. A local linear smoothing technique combining both continuous and discrete kernel functions is introduced to estimate the conditional quantile function. We propose using a fully data-driven cross-validation approach to choose the bandwidths, and further derive the asymptotic optimality theory. In addition, we also establish the asymptotic distribution and uniform consistency (with convergence rates) for the local linear conditional quantile estimators with the data-dependent optimal bandwidths. Simulations show that the proposed approach compares well with some existing methods. Finally, an empirical application with the data taken from the IMDb website is presented to analyze the relationship between box office revenues and online rating scores. Supplementary materials for this article are available online.}, number={3}, journal={JOURNAL OF BUSINESS & ECONOMIC STATISTICS}, author={Li, Degui and Li, Qi and Li, Zheng}, year={2021}, month={Jul}, pages={741–756} }
 @article{chen_li_ming_zhu_2021, title={The Incentive Game Under Target Effects in Ridesharing: A Structural Econometric Analysis}, ISSN={["1526-5498"]}, DOI={10.1287/msom.2021.1002}, abstractNote={ Problem definition: We study a ridesharing platform’s optimal bonus-setting decisions for capacity and profit maximization problems in which drivers set daily income targets. Academic and Practical Relevance: Sharing-economy companies have been providing monetary rewards to incentivize self-scheduled drivers to work longer. We study the effectiveness of the monetary bonus scheme in the context of the ridesharing industry, where the drivers are highly heterogeneous and set income targets. Methodology: We model a driver’s decision-making processes and the platform’s optimization problem as a Stackelberg game. Then, utilizing comprehensive datasets obtained from a leading ridesharing platform, we develop a novel empirical strategy to provide evidence on the existence of drivers’ income-targeting behavior through a reduced-form and structural analysis. Furthermore, we perform a counterfactual analysis to calculate the optimal bonus rates for different scenarios by using the characteristics of heterogeneous drivers derived from the estimation outcomes. Results: Our theoretical model suggests that the drivers’ working hours do not increase monotonically with the bonus rate under the target effect and that the platform may not use all its budget on bonuses to maximize capacity or profit. We empirically demonstrate that the drivers engage in income-targeting behavior, and furthermore, we estimate the income targets for heterogeneous drivers. Through counterfactual analysis, we illustrate how the optimal bonus scheme varies when the platform faces different driver compositions and market conditions. We also find that, compared with the platform’s previous bonus setting, the optimal bonus strategy improves the capacity level during peak hours by as much as 26%, boosting the total profit by $4.3 million per month. Managerial implications: It is challenging to develop a flexible self-scheduled supply of drivers that can match the ever-changing demand and maintain the market share of the ridesharing platform. When offering monetary bonuses to incentivize drivers to work longer, the drivers’ income-targeting behavior can undermine the effectiveness of such bonus schemes. The platform needs to understand the heterogeneity of drivers’ behavioral preferences regarding monetary rewards to design an effective bonus strategy. }, journal={M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT}, author={Chen, Xirong and Li, Zheng and Ming, Liu and Zhu, Weiming}, year={2021}, month={Oct} }
 @article{chen_gao_li_2018, title={A data-driven bandwidth selection method for the smoothed maximum score estimator}, volume={170}, ISSN={["1873-7374"]}, DOI={10.1016/j.econlet.2018.05.024}, abstractNote={Binary response regression models are useful in many economic and statistical applications. Horowitz (1992) proposes a semi-parametric estimation method, which is a smoothed version of, and has a faster convergence rate than, Manski’s maximum score estimator. The method for selecting the smoothing parameter (bandwidth) here is analogous to the plug-in method in kernel density estimation. It requires initial “pilot” values of the bandwidth to obtain the optimal bandwidth. However, this method has the disadvantage of not being fully data-driven. In this paper, we propose a data-driven bandwidth selection method by minimizing a cross-validated criterion function. Our simulation results show that the proposed method performs better than some existing methods.}, journal={ECONOMICS LETTERS}, author={Chen, Xirong and Gao, Wenzheng and Li, Zheng}, year={2018}, month={Sep}, pages={24–26} }
 @article{li_liu_li_2017, title={Nonparametric Knn estimation with monotone constraints}, volume={36}, ISSN={["1532-4168"]}, DOI={10.1080/07474938.2017.1307904}, abstractNote={ABSTRACT The K-nearest-neighbor (Knn) method is known to be more suitable in fitting nonparametrically specified curves than the kernel method (with a globally fixed smoothing parameter) when data sets are highly unevenly distributed. In this paper, we propose to estimate a nonparametric regression function subject to a monotonicity restriction using the Knn method. We also propose using a new convergence criterion to measure the closeness between an unconstrained and the (monotone) constrained Knn-estimated curves. This method is an alternative to the monotone kernel methods proposed by Hall and Huang (2001), and Du et al. (2013). We use a bootstrap procedure for testing the validity of the monotone restriction. We apply our method to the “Job Market Matching” data taken from Gan and Li (2016) and find that the unconstrained/constrained Knn estimators work better than kernel estimators for this type of highly unevenly distributed data.}, number={6-9}, journal={ECONOMETRIC REVIEWS}, author={Li, Zheng and Liu, Guannan and Li, Qi}, year={2017}, pages={988–1006} }
 @article{lin_cai_li_su_2015, title={Optimal smoothing in nonparametric conditional quantile derivative function estimation}, volume={188}, ISSN={0304-4076}, url={http://dx.doi.org/10.1016/J.JECONOM.2015.03.014}, DOI={10.1016/J.JECONOM.2015.03.014}, abstractNote={Marginal effect in nonparametric quantile regression is of special interest as it quantitatively measures how one unit change in explanatory variable heterogeneously affects dependent variable ceteris paribus at distinct quantiles. In this paper, we propose a data-driven bandwidth selection procedure based on the gradient of an unknown quantile regression function. Our method delivers the bandwidth with the oracle property in the sense that it is asymptotically equivalent to the optimal bandwidth if the true gradient were known. The results of Monte Carlo simulations are reported, and the finite sample performance of our proposed method confirms our theoretical analysis. An empirical application is also provided, showing that our proposed method delivers more reasonable and reliable quantile derivative estimates than traditional cross validation method.}, number={2}, journal={Journal of Econometrics}, publisher={Elsevier BV}, author={Lin, Wei and Cai, Zongwu and Li, Zheng and Su, Li}, year={2015}, month={Oct}, pages={502–513} }
 @article{li_su_zhang_2014, title={Profile least squares estimation of a partially linear time trend model with weakly dependent data}, volume={125}, ISSN={0165-1765}, url={http://dx.doi.org/10.1016/J.ECONLET.2014.10.030}, DOI={10.1016/J.ECONLET.2014.10.030}, abstractNote={We consider a partially linear time trend model with weakly dependent data. We show that the semiparametric estimator for the time trend coefficient has the same rate of convergence as in the parametric time trend model case. We also show that the asymptotic variance reaches the semiparametric efficient bound. The Monte Carlo simulations strongly support our theoretical analysis.}, number={3}, journal={Economics Letters}, publisher={Elsevier BV}, author={Li, Zheng and Su, Li and Zhang, Daiqiang}, year={2014}, month={Dec}, pages={404–407} }