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Principal component (PC) techniques are commonly used to improve the small sample properties of the linear instrumental variables (IV) estimator. Carrasco (2012) argue that PC type methods provide a natural ranking of instruments with which to reduce the size of the instrument set. This chapter shows how reducing the size of the instrument based on PC methods can lead to poor small sample properties of IV estimators. A new approach to ordering instruments termed ‘normalized principal components’ (NPCs) is introduced to overcome this problem. A simulation study shows the favourable small samples properties of IV estimators using NPC, methods to reduce the size of the instrument relative to PC. Using NPC we provide evidence that the IV setup in Angrist and Krueger (1992) may not suffer the weak instrument problem.

This paper challenges and expands commonplace assumptions about problems of time and temporality in emergencies. In traditional emergency powers theory “emergency time” is predominantly an “exceptional time.” The problem is that there is “no time” and the solution is limited “in time”: exceptional behavior is allowed for a special time only, until the emergency is over, or according to formal sunset clauses. But what is characteristic of many emergencies is not the problem of “no time” but the ways in which time is legally structured and framed to handle them. Using the Israeli High Court of Justice 1999 decision on the use of physical interrogation methods under conditions of necessity, this paper illustrates how legally significant emergency-time structures that lay beyond the problematic of exceptional time, gravely implicate the way that “exceptional measures” are practiced and regularized.

Structural models of demand founded on the classic work of Berry, Levinsohn, and Pakes (1995) link variation in aggregate market shares for a product to the influence of product attributes on heterogeneous consumer tastes. We consider implementing these models in settings with complicated products where consumer preferences for product attributes are sparse, that is, where a small proportion of a high-dimensional product characteristics influence consumer tastes. We propose a multistep estimator to efficiently perform uniform inference. Our estimator employs a penalized pre-estimation model specification stage to consistently estimate nonlinear features of the BLP model. We then perform selection via a Triple-LASSO for explanatory controls, treatment selection controls, and instrument selection. After selecting variables, we use an unpenalized GMM estimator for inference. Monte Carlo simulations verify the performance of these estimators.

In a paper shared at the 2004 Canadian Society for the Study of Education (CSSE), Marie Battiste urged Canadian academics and policy makers to become part of a transformative process of reconstructing Canada's colonial education system which she describes as shaping “Indigenous peoples’ trauma and disconnection with many aspects of education and themselves” (p. 2). Battiste calls for the repositioning of Indigenous knowledges in post-secondary institutions, a process through which institutional structures and practices, curriculum foundations, and traditions are substantially changed and, in particular, that these are changed in ways that value and engage the capacities of Aboriginal students. Battiste's argument is significant for both Aboriginal post-secondary students and for their communities.

In a recent paper, Hausman, Newey, Woutersen, Chao, and Swanson (2012) propose a new estimator, HFUL (Heteroscedasticity robust Fuller), for the linear model with endogeneity. This estimator is consistent and asymptotically normally distributed in the many instruments and many weak instruments asymptotics. Moreover, this estimator has moments, just like the estimator by Fuller (1977). The purpose of this note is to discuss at greater length the existence of moments result given in Hausman et al. (2012). In particular, we intend to answer the following questions: Why does LIML not have moments? Why does the Fuller modification lead to estimators with moments? Is normality required for the Fuller estimator to have moments? Why do we need a condition such as Hausman et al. (2012), Assumption 9? Why do we have the adjustment formula?

Drawing on a view of children's and teachers’ identities as stories to live by, the authors use one field text, taken from a year-long narrative inquiry, to show how children's and teachers’ stories to live by interact with milieu and subject matter in classroom curriculum making. Tensions around negotiating a curriculum of lives are identified as children's stories bump against teachers’ stories. Three children's stories to live by are represented through a set of images in found poetry. We return to the curriculum-making moment with wonders about each child's evolving stories to live by in relation with the particular subject matter. We outline four methodological dilemmas and ethical dilemmas encountered in studying multiple participants’ experiences nested within social, cultural and institutional narratives.

Statistical inference (estimation and testing) for the stochastic volatility (SV) model Taylor (1982, 1986) is challenging, especially likelihood-based methods which are difficult to apply due to the presence of latent variables. The existing methods are either computationally costly and/or inefficient. In this paper, we propose computationally simple estimators for the SV model, which are at the same time highly efficient. The proposed class of estimators uses a small number of moment equations derived from an ARMA representation associated with the SV model, along with the possibility of using “winsorization” to improve stability and efficiency. We call these ARMA-SV estimators. Closed-form expressions for ARMA-SV estimators are obtained, and no numerical optimization procedure or choice of initial parameter values is required. The asymptotic distributional theory of the proposed estimators is studied. Due to their computational simplicity, the ARMA-SV estimators allow one to make reliable – even exact – simulation-based inference, through the application of Monte Carlo (MC) test or bootstrap methods. We compare them in a simulation experiment with a wide array of alternative estimation methods, in terms of bias, root mean square error and computation time. In addition to confirming the enormous computational advantage of the proposed estimators, the results show that ARMA-SV estimators match (or exceed) alternative estimators in terms of precision, including the widely used Bayesian estimator. The proposed methods are applied to daily observations on the returns for three major stock prices (Coca-Cola, Walmart, Ford) and the S&P Composite Price Index (2000–2017). The results confirm the presence of stochastic volatility with strong persistence.