Tricks with metrics: combining statistics for improved inference in regression analysis
提出结合似然比和Wald检验统计量的方法，在小样本回归模型中实现更精确的参数推断，并通过蒙特卡洛模拟和肯尼亚移动货币采用案例验证其优越性。
.In maximum likelihood methods, the three classical tests statistics are often unreliable for inference in small samples, even under the correct model specification. In this article, I discuss how likelihood ratio and Wald test statistics can be combined to obtain highly improved parameter inference in regression models with small samples. I consider modifications obtained from both the Barndorff-Nielsen and the Lugannani and Rice likelihood approximations, and I show how they can produce highly accurate parameter inference in a general (possibly non linear) regression model with possibly non spherical disturbances. I discuss the underlying theory and provide Monte Carlo simulations demonstrating the superior accuracy of the proposed procedures over the first-order classical likelihood methods (i.e., the signed log-likelihood ratio test and the Wald test). An empirical application to a regression model of mobile money (“M-pesa”) adoption in Kenya is provided as an illustration of the usefulness of these methods in practice.