Wei Gao

COVID‐19 crisis has significantly impacted global economic growth. Assessing the economic impacts of various policies is crucial to better public health management (PHM). To incorporate specific PHM policies into a unified framework, prevention and control intensity (PCI) is introduced to quantify the intensity of PHM. Under different PCI, we construct a statistical framework to estimate each economy's counterfactual output, thereby providing two indicators to assess the effectiveness of PHM adopted by certain economies. Empirical analysis measures the effectiveness of PHM in each of the 137 economies during COVID‐19 in 2020. Further analysis is conducted through robustness tests and regression discontinuity design. Aggregated results from 137 economies indicate that COVID‐19 may have caused global economic losses far exceeding 5.47 trillion US dollars in 2020. Statistical indicators reveal that 39 economies reduced economic loss through PHM, while 25 economies achieved relatively ideal economic output targets. These findings suggest that moderate prevention and control measures can significantly reduce economic losses and highlight the necessity for most countries to strengthen their PHM strategies to enhance economic resilience and recovery.

Sufficient dimension reduction (SDR) is a valuable approach for handling high-dimensional data. Outer Product Gradient (OPG) is an popular approach. However, because of focusing the mean regression function, OPG may ignore some directions of central subspace (CS) when the distribution of errors is symmetric about zero. The mode of a distribution can provide an important summary of data. A Local Modal OPG (LMOPG) and its algorithm through mode regression are proposed to estimate the basis of CS with skew errors distribution. The estimator shows the consistent and asymptotic normal distribution under some mild conditions. Monte Carlo simulation is used to evaluate the performance and demonstrate the efficiency and robustness of the proposed method.

Public health events (PHEs) have emerged as significant threats to human life, health, and economic growth. PHEs, such as COVID-19, have prompted a reevaluation for enhanced regular prevention and control (RPC). In this study, we focus on the core concept of prevention and control intensity (PCI), and establish a neoclassical economic growth model from the long-term and macro perspective to balance life protection and economic growth. The model construct the mechanism of PCI on economic growth through population dynamics and capital accumulation under the backdrop of RPC for PHEs. We find the conditions for PCI when the economy achieves steady state, and provides an algorithm establishing the optimal strategy that maximises per capita disposable income based on the optimal PCI and consumption. Simulation result quantifies an inverted U-shaped relationship between PCI and capital per capita, output per capita and consumption per capita in the steady state. The model suggests that, given the PHEs of inducing potential unemployment shock, it is worthwhile to combine the implementation of moderate PCI with coordinated policies of income distribution.

In this paper, we propose two simple yet efficient computational algorithms to obtain approximate optimal designs for multi-dimensional linear regression on a large variety of design spaces. We focus on the two commonly used optimal criteria, D- and A-optimal criteria. For D-optimality, we provide an alternative proof for the monotonic convergence for D-optimal criterion and propose an efficient computational algorithm to obtain the approximate D-optimal design. We further show that the proposed algorithm converges to the D-optimal design and then proves that the approximate D-optimal design converges to the continuous D-optimal design under certain conditions. For A-optimality, we provide an efficient algorithm to obtain approximate A-optimal design and conjecture the monotonicity of the proposed algorithm. Numerical comparisons suggest that the proposed algorithms perform well and they are comparable or superior to some existing algorithms.

Treatment effects estimation is one of the crucial mainstays in medical and epidemiological studies. Ignorance of the existence of confounders may result in biased estimators. The issue will become more serious and complicated if the treatment is endogenous (i.e., the presence of unobserved confounders). In this article, we propose a new treatment effects estimator for binary treatments in observational studies in the presence of unobserved confounders. The proposed estimator is consistent and asymptotically normally distributed. A statistic is also developed for testing the existence of treatment effects. Simulation studies show that the proposed estimator is stable for various unobserved confounding settings and the distribution of error terms. Finally, we apply our proposed methodologies to a low birthweight data set which yields different conclusions with and without the consideration of possible unobserved confounders.

We study a statistical model for panel data with unobservable grouped factor structures which are correlated with the regressors and whose group membership can be unknown. We assume the factor loadings belong to different subspaces and consider the subspace clustering for factor loadings. We propose a method called least‐squares subspace clustering (LSSC) to estimate the model parameters by minimizing the least‐squares distance while simultaneously performing the subspace clustering. We establish the consistency of our proposed subspace clustering method and study the asymptotic properties of our proposed estimators under certain conditions. Monte Carlo simulation studies illustrate the advantages of our proposed methodologies. To choose the subspace dimensions consistently, we use a model selection criterion. We also outline further considerations for situations when the number of subspaces and the dimensions of factors are unknown. For illustrative purposes, our proposed methods are applied to study the linkage between income and democracy across countries.

The outbreak of Covid‐19 in China during the Spring Festival of 2020 has changed life as we knew it. To explore its impact on China's economy, we analyse the daily railway passenger volume data during the Spring Festival travel rush and establish two RegARMA models to predict GDP in the first quarter. The models forecast China might lose 4.8 trillion yuan in the first quarter of 2020 due to Covid‐19, an expected decrease of 20.69 percent (year‐on‐year drop 15.60 percent). However, comparing our forecast GDP without Covid‐19 (23.2 trillion yuan) with the real GDP (20.65 trillion yuan), there is a smaller drop of 2.55 trillion yuan, a gap of 12.35 percent. The reason for this overestimation is that some positive factors, including macro‐control policies, are neglected in these models. With the global spread of Covid‐19, China should adopt a policy of focusing on balancing both domestic and external affairs against the instability of the world economy.