Qingsong Yao Publications

This paper develops a framework for fast online inference on semiparametric models with large sample sizes and possibly many covariates. The computational algorithm itself is the object of statistical study: after a globally consistent warm start in the first phase, the path of averaged online iterates generated in the second phase automatically delivers estimators with optimal convergence rates and valid confidence sets. Both phases require only a single pass over the data stream and are well suited to streaming data or to settings with storage/privacy constraints. For semiparametric monotone index models, the averaged trajectory of the second phase lead to estimators that are automatically orthogonalized and satisfy the laws of the iterated logarithms, and policy functionals are updated along the same trajectory at negligible additional cost. The averaged trajectories satisfy functional central limit theorems, which yield fast online inference via random scaling and bypass the explicit variance estimation that complicates inference for semiparametric models. Applied to a fixed large sample, our online algorithm achieves substantial computational gains over corresponding offline procedures without sacrificing statistical performance. Monte Carlo experiments show adequate behavior. Our methods are applied to 19 million traffic-stop online records from the North Carolina State Patrol (Pierson et al. 2020) and to the international trade data of Helpman et al. (2008) with over 300 regressors. We also compare our estimator to its parametric benchmark in both empirical illustrations.

In this paper, we propose a triple (or double-debiased) Lasso estimator for inference on a low-dimensional parameter in high-dimensional linear regression models. The estimator is based on a moment function that satisfies not only first- but also second-order Neyman orthogonality conditions, thereby eliminating both the leading bias and the second-order bias induced by regularization. We derive an asymptotic linear representation for the proposed estimator and show that its remainder terms are never larger and are often smaller in order than those in the corresponding asymptotic linear representation for the standard double Lasso estimator. Because of this improvement, the triple Lasso estimator often yields more accurate finite-sample inference and confidence intervals with better coverage. Monte Carlo simulations confirm these gains. In addition, we provide a general recursive formula for constructing higher-order Neyman orthogonal moment functions in Z-estimation problems, which underlies the proposed estimator as a special case.

Caregiving is a service provided for children with the primary objective of taking care of them and ensuring that they are safe and have opportunities to learn and develop positive relationships with their caregivers and peers while their parents are away. Caregiving takes the forms of home-based care, centre-based care, school-based care, family child care and family, friend, and neighbour (FFN) care. The paper utilises preliminary findings on school attendance from a randomised controlled trial on the effects of a preschool intervention on child learning and women’s economic empowerment in Tharaka Nithi County in school-based care. The research sought to test whether a preschool-based intervention in a rural setting in Kenya influences child development and women’s labour market participation in a cost-effective manner. The project examines the impact of allowing three-year-old children to attend preschool versus the regular pre-primary education programming, which allows children aged 4 years and above to attend preschool. Implementation of the intervention started in January 2024 in 60 intervention schools where five three-year-old children were admitted to a playgroup (PG) in the pre-primary one (PP1) class. Twelve mentors and sixty caregivers were recruited and trained alongside sixty PP1 teachers from the sampled preschools to implement an adapted PP1 curriculum. The twelve mentors coached teachers weekly on the implementation of the curriculum in the five schools assigned to them. This paper presents preliminary findings on preschool attendance for the PG and PP1 children based on weekly attendance data from term one and term two of the 2024 school calendar year on the day the mentors visited the school. Findings reveal that school attendance was low during school openings, midterm breaks, and the last weeks before the schools closed. Public holidays, as well as extracurricular activities coupled with children being sent home for school levies, also contributed to children not attending school regularly. The findings further show that the attendance rate in term one was slightly higher than in term two.