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Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

This chapter discusses what special education means for students with visual impairments (that is, those who are blind or have low vision) including what is being done and how traditions are maintained. More specifically, this chapter explores the importance of advancing values for the diverse population of students with visual impairments, focusing on cultivation of supportive, inclusive, and collaborative educational environments that continue to stand the test of time. This chapter highlights the increasing heterogeneity of this population of students and specific instructional strategies to support the cultural and linguistic diversity of learners with visual impairments in today's classrooms. This chapter also discusses the significance of promoting core concepts that are rooted in a traditional and specialized instructional framework for students who are visually impaired.