Continuous-time models have found broad applications in many core areas of economics and finance. This chapter first briefly introduces the applications of the continuous-time models for modeling the dynamics of the short-term interest rates. While many estimation methods have been proposed to estimate continuous-time models with discrete samples over the past 40 years, almost all suffer from finite-sample bias. The bias problem is particularly severe for the mean-reversion parameter, which measures the persistence level of the interest-rate process. Moreover, such bias propagates and leads to considerable bias in price calculations of the interest-rate contingent claims, such as bonds and bond options. The focus of this chapter is to give a detailed review of the bias issue. Two bias-correction methods are discussed: the jackknife method and the indirect inference method, which can effectively reduce the estimation bias of the mean-reversion parameter and the bias in pricing contingent claims. Monte Carlo studies are provided to illustrate the characteristics of the bias and investigate the performance of the two bias-correction methods.

Recent findings show that it is possible in some cases to robustly and durably change implicit impressions of novel individuals. This work presents a challenge to long-standing theoretical assumptions about implicit impressions, and raises new research directions for changing and reducing implicit bias toward outgroups. Namely, implicit impressions of newly encountered individuals and groups are more amenable to robust change and updating than previously assumed, and some of the lessons from this work point to when and how we might try to change implicit bias toward well-known and familiar stigmatized groups and individuals.