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This chapter studies the dynamic responses of the conditional quantiles and their applications in macroeconomics and finance. The authors build a multi-equation autoregressive conditional quantile model and propose a new construction of quantile impulse response functions (QIRFs). The tool set of QIRFs provides detailed distributional evolution of an outcome variable to economic shocks. The authors show the left tail of economic activity is the most responsive to monetary policy and financial shocks. The impacts of the shocks on Growth-at-Risk (the 5% quantile of economic activity) during the Global Financial Crisis are assessed. The authors also examine how the economy responds to a hypothetical financial distress scenario.

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilizing tail event information. Financial Risk Meter (FRM) is based on least absolute shrinkage and selection operator quantile regression designed to capture tail event co-movements. The FRM focus lies on understanding active set data characteristics and the presentation of interdependencies in a network topology. Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM indices detect systemic risk at selected areas and identify risk factors. In practice, FRM is applied to the return time series of selected financial institutions and macroeconomic risk factors. The authors identify companies exhibiting extreme “co-stress” as well as “activators” of stress. With the SRM@EuroArea, the authors extend to the government bond asset class, and to credit default swaps with FRM@iTraxx. FRM is a good predictor for recession probabilities, constituting the FRM-implied recession probabilities. Thereby, FRM indicates tail event behavior in a network of financial risk factors.

Map of this detour: This is one of a series of detours compelled by consideration of inheritance law as an aspect of cultural transmission. ¹ This course draws attention to three problematic time forms (temporalities) through which the “self” and its relations with history are often written and read. These implicit time forms are all too common and all too easily go unrecognized. Each involves the illusion of some kind of exalted and immediate convergence between the self (the subject) and an object of exaggerated importance to this self (the world, the universe, the metaphysical or artistic beyond, the origin, etc.). Three figures are explored here: that of Hercules in Hegel’s Aesthetics, and those of Adrian and Breisacher in Thomas Mann’s Doctor Faustus. Each of these invites attention to a different temporality through which an exalted convergence may be imagined: the first involves a fantasy of immediate belonging to the whole of history, the second, that of escape forward from history (toward a self-created “ultimate” object), and the third, that of return to fullness in origin (before history). This detour also suggests ways of reading history (including “reading for mana through glances,” which will be explained) that protect against the problems just described. The detour closes considering implications of all of the above for U.S. inheritance law. The tutor text for this last leg is François Mauriac’s Le noeud de vipères.

Are education systems converging toward a global model of teacher education or do local models tend to predominate in spite of attempts to reform them? How much do global, national, and local cultures shape and condition future teachers’ opportunities to learn to teach? How do these opportunities influence teachers’ pedagogical content knowledge? In this chapter we use data from the IEA’s first study of the effectiveness of pre-service teacher education in order to investigate teacher education policy, program structure, and outcomes. Using multilevel modeling we found that across countries individual characteristics have a similar and powerful influence on what future teachers come to know at the end of their pre-service programs. The effects of teacher education curriculum on future teachers’ mathematics pedagogical content knowledge reaffirm the prevalence of local cultures on the implementation of an increasingly globalized ideal. We conclude that while the provision of teacher education shares many common features in goals and structure across countries, it is strongly influenced by local conditions and norms, and by cultural notions of the knowledge that is considered essential – framing how quality is to be defined and operationalized – when learning to teach.

This chapter proposes a vector autoregressive VAR model with structural shocks (SVAR) that are identified using sign restrictions, and whose distribution is subject to time varying skewness. The authors also present an efficient Bayesian algorithm to estimate the model. The model allows tracking joint asymmetric risks to macroeconomic variables included in the SVAR, and provides a structural narrative to the evolution of those risks. When faced with euro area data, our estimation suggests that there has been a significant variation in the skewness of demand, supply and monetary policy shocks. Such variation can explain a significant proportion of the joint dynamics of real GDP growth and inflation, and also generates important asymmetric tail risks in those macroeconomic variables. Finally, compared to the literature on growth- and inflation-at-risk, the authors find that financial stress indicators are not enough to explain all the macroeconomic tail risks.

This chapter uses an application to explore the utility of Bayesian quantile regression (BQR) methods in producing density nowcasts. Our quantile regression modeling strategy is designed to reflect important nowcasting features, namely the use of mixed-frequency data, the ragged-edge, and large numbers of indicators (big data). An unrestricted mixed data sampling strategy within a BQR is used to accommodate a large mixed-frequency data set when nowcasting; the authors consider various shrinkage priors to avoid parameter proliferation. In an application to euro area GDP growth, using over 100 mixed-frequency indicators, the authors find that the quantile regression approach produces accurate density nowcasts including over recessionary periods when global-local shrinkage priors are used.