A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression

Graph-theoretic methods of causal search based in the ideas of Pearl (2000), Spirtes, Glymour, and Scheines (2000), and others have been applied by a number of researchers to economic data, particularly by Swanson and Granger (1997) to the problem of finding a data-based contemporaneous causal order for the structural autoregression (SVAR), rather than, as is typically done, assuming a weakly justified Choleski order. Demiralp and Hoover (2003) provided Monte Carlo evidence that such methods were effective, provided that signal strengths were sufficiently high. Unfortunately, in applications to actual data, such Monte Carlo simulations are of limited value, since the causal structure of the true data-generating process is necessarily unknown. In this paper, we present a bootstrap procedure that can be applied to actual data (i.e., without knowledge of the true causal structure). We show with an applied example and a simulation study that the procedure is an effective tool for assessing our confidence in causal orders identified by graph-theoretic search procedures.vector autoregression (VAR), structural vector autoregression (SVAR),causality, causal order, Choleski order, causal search algorithms, graph-theoretic methods

The impacts of cloud computing adoption at Higher Education institutions: a SWOT analysis

The integration of advanced technologies within education has frequently enhanced teaching. In higher education it is not a surprise that using the latest developments in cloud computing improves learning practices and thus ensures they are more interactive, available, and convenient. The ease of integration, collaboration, and sharing of information and knowledge made possibleby cloud computing will be further enhanced if this technical advancement is used wisely and in a foolproof manner. In this paper, a SWOT analysis of the impact of cloud computing on higher education methodologies is presented. A SWOT analysis is here demonstrated to be a helpful guide in decision-making for all higher education institutions when considering the migration of their present learning systems to cloud based systems

On the Physical Hilbert Space of Loop Quantum Cosmology

In this paper we present a model of Riemannian loop quantum cosmology with a self-adjoint quantum scalar constraint. The physical Hilbert space is constructed using refined algebraic quantization. When matter is included in the form of a cosmological constant, the model is exactly solvable and we show explicitly that the physical Hilbert space is separable consisting of a single physical state. We extend the model to the Lorentzian sector and discuss important implications for standard loop quantum cosmology