Fisher consistent estimation of regression parameter in the proportional mean residual life model with frailty

In the article, we consider the Fisher consistent estimation of the regression parameters in the proportional mean residual life model with arbitrary frailty. It is discussed that conventional estimation procedures, such as the maximum likelihood estimation or Cox’s approach, which are employed in common regression models, may also yield consistent inference in the extended models.

High-dimensional semi-supervised learning: in search of optimal inference of the mean

A fundamental challenge in semi-supervised learning lies in the observed data’s disproportional size when compared with the size of the data collected with missing outcomes. An implicit understanding is that the dataset with missing outcomes, being significantly larger, ought to improve estimation and inference. However, it is unclear to what extent this is correct. We illustrate one clear benefit: root-n inference of the outcome’s mean is possible while only requiring a consistent estimation of the outcome, possibly at a rate slower than root-n. This is achieved by a novel k-fold cross-fitted, double robust estimator. We discuss both linear and nonlinear outcomes. Such an estimator is particularly suited for models that naturally do not admit root-n consistency, such as high-dimensional, nonparametric, or semiparametric models. We apply our methods to the heterogeneous treatment effects.

Impact of Fintech on Bank Risk-Taking: Evidence from China

This article focuses on the relationship between Fintech and bank risk-taking behavior. Since Robo-Advisor is one of the mature applications of Fintech, we found that the development of Fintech will have a greater impact on small and medium-sized banks through the establishment of a Robo-Advisor model. This paper uses a benchmark regression model to analyze the municipal digital financial inclusion index compiled by Peking University and the annual report data of 155 small and medium-sized banks from 2011 to 2016. We found that the development of Fintech has significantly reduced bank risk-taking level. This result is still valid after the robustness test of replacing the bank’s risk-taking index and replacing the Fintech development index. We used the urban innovation index as an instrumental variable to deal with the endogenous problem, and obtained consistent estimation results. The test of the intermediary effect shows that the development of Fintech will affect the bank risk-taking through channels such as the bank’s internal interest margin, management capabilities, the bank’s external competition intensity, and residents’ saving willingness. Heterogeneity analysis shows the reduction effect of Fintech on bank risk-taking is more pronounced in banks in eastern and western regions in China, the large banks and the urban commercial banks.

msreg: A command for consistent estimation of linear regression models using matched data

Economists often use matched samples, especially when dealing with earning data where some observations are missing in one sample and need to be imputed from another sample. Hirukawa and Prokhorov (2018, Journal of Econometrics 203: 344–358) show that the ordinary least-squares estimator using matched samples is inconsistent and propose two consistent estimators. We describe a new command, msreg, that implements these two consistent estimators based on two samples. The estimators attain the parametric convergence rate if the number of continuous matching variables is no greater than four.