Foreign exchange risk premia: from traditional to state-space analyses

This paper examines foreign exchange risk premia from simple univariate regressions to the state-space method. The adjusted traditional regressions properly figure out the existence and time-evolving property of the risk premia. Successively, the state-space estimations overall are quite rationally competent in examining the essence of time variability of the unobservable risk premia. To be more precise, the coefficients on the lagged estimated time-series are significant and the disturbance combined from the observation and transition equations in the state-space system, rational and premium errors, respectively, is statistically white noise. Such the two residuals are discovered to move oppositely with their covariance approaching zero suggested by the empirics. Besides, foreign exchange risk premia are projected and found significantly stationary at level and relatively volatile throughout time with some clustering. This volatility is however not quite dominant in the deviations of forward prediction errors

Foreign exchange risk premia: from traditional to state-space analyses

This paper examines foreign exchange risk premia from simple univariate regressions to the state-space method. The adjusted traditional regressions properly figure out the existence and time-evolving property of the risk premia. Successively, the state-space estimations overall are quite rationally competent in examining the essence of time variability of the unobservable risk premia. To be more precise, the coefficients on the lagged estimated time-series are significant and the disturbance combined from the observation and transition equations in the state-space system, rational and premium errors, respectively, is statistically white noise. Such the two residuals are discovered to move oppositely with their covariance approaching zero suggested by the empirics. Besides, foreign exchange risk premia are projected and found significantly stationary at level and relatively volatile throughout time with some clustering. This volatility is however not quite dominant in the deviations of forward prediction errors

Performance and state-space analyses of systems using Petri nets

The goal of any modeling methodology is to develop a mathematical description of a system that is accurate in its representation and also permits analysis of structural and/or performance properties. Inherently, trade-offs exist between the level detail in the model and the ease with which analysis can be performed. Petri nets (PN's), a highly graphical modeling methodology for Discrete Event Dynamic Systems, permit representation of shared resources, finite capacities, conflict, synchronization, concurrency, and timing between state changes. By restricting the state transition time delays to the family of exponential density functions, Markov chain analysis of performance problems is possible. One major drawback of PN's is the tendency for the state-space to grow rapidly (exponential complexity) compared to increases in the PN constructs. It is the state space, or the Markov chain obtained from it, that is needed in the solution of many problems. The theory of state-space size estimation for PN's is introduced. The problem of state-space size estimation is defined, its complexities are examined, and estimation algorithms are developed. Both top-down and bottom-up approaches are pursued, and the advantages and disadvantages of each are described. Additionally, the author's research in non-exponential transition modeling for PN's is discussed. An algorithm for approximating non-exponential transitions is developed. Since only basic PN constructs are used in the approximation, theory already developed for PN's remains applicable. Comparison to results from entropy theory show the transition performance is close to the theoretic optimum. Inclusion of non-exponential transition approximations improves performance results at the expense of increased state-space size. The state-space size estimation theory provides insight and algorithms for evaluating this trade-off

State Space Analysis and its Connection to the Classroom

Discrete dynamical systems have been used to theoretically model the complex dynamics of classrooms. While time-series analyses of these models has yielded some insights, state space analyses can yield additional insights; this paper will explore state space analyses and their application to classroom situations. One benefit of state space analysis is that it allows simultaneous exploration of multiple time-series, and so can more easily provide information about divergence and convergence of paths. Additionally, state space analysis, more easily than time-series analysis, can provide information about the existence of multiple paths leading toward a desired state. Further, state space analysis can identify different regimes of behaviors, finding boundaries near which there may be divergent behaviors, and also using those regimes to define a (sometimes) relatively small number of archetypical behaviors. This is particularly useful in tracking behaviors at a microgenetic level, since multiple initial conditions may get to the same (or very close) final states, but in dramatically different ways, and these different routes may have implications for future classroom experiences. Because of these advantages, state space analysis can be used to inform attempts at differentiated instruction in a classroom, assist modelers in identifying appropriate parameter scales, and provide guidance for empirical studies of classroom learning. These ideas will be illustrated through state space analysis of an existing model of teacher-student interactions, identifying four regimes of behaviors, and leading to several implications for classroom practice and research

Can enlightenment be traced to specific neural correlates, cognition, or behavior? No, and (a qualified) Yes

The field of contemplative science is rapidly growing and integrating into the basic neurosciences, psychology, clinical sciences, and society-at-large. Yet the majority of current research in the contemplative sciences has been divorced from the soteriological context from which these meditative practices originate and has focused instead on clinical applications with goals of stress reduction and psychotherapeutic health. In the existing research on health outcomes of mindfulness-based clinical interventions, for example, there have been almost no attempts to scientifically investigate the goal of enlightenment. This is a serious oversight, given that such profound transformation across ethical, perceptual, emotional, and cognitive domains are taken to be the natural outcome and principle aim of mindfulness practice in the traditional Buddhist contexts from which these practices are derived. If short-term interventions as short as a few sessions are now beginning to produce neuroplastic changes, it may be that even in secular contexts, practitioners are already developing states and traits that are associated with progress toward enlightenment. In order to carefully assess the potential effects of meditative interventions it is of singular importance to ask whether enlightenment can be traced to specific neural correlates, cognition, or behavior

On-line analysis capabilities developed to support the AFW wind-tunnel tests

A variety of on-line analysis tools were developed to support two active flexible wing (AFW) wind-tunnel tests. These tools were developed to verify control law execution, to satisfy analysis requirements of the control law designers, to provide measures of system stability in a real-time environment, and to provide project managers with a quantitative measure of controller performance. Descriptions and purposes of the developed capabilities are presented along with examples. Procedures for saving and transferring data for near real-time analysis, and descriptions of the corresponding data interface programs are also presented. The on-line analysis tools worked well before, during, and after the wind tunnel test and proved to be a vital and important part of the entire test effort

Solving DSGE Models with a Nonlinear Moving Average

We introduce a nonlinear infinite moving average as an alternative to the standard state-space policy function for solving nonlinear DSGE models. Perturbation of the nonlinear moving average policy function provides a direct mapping from a history of innovations to endogenous variables, decomposes the contributions from individual orders of uncertainty and nonlinearity, and enables familiar impulse response analysis in nonlinear settings. When the linear approximation is saddle stable and free of unit roots, higher order terms are likewise saddle stable and first order corrections for uncertainty are zero. We derive the third order approximation explicitly and examine the accuracy of the method using Euler equation tests.Perturbation, nonlinear impulse response, DSGE, solution methods

Space Shuttle Proximity Operation Sensor Study

The performance of the Kuband radar was analyzed in detail, and the performance was updated and summarized. In so doing, two different radar design philosophies were described, and the corresponding differences in losses were enumerated. The resulting design margins were determined for both design philosophies and for both the designated and nondesignated range modes of operation. In some cases, the design margin was about zero, and in other cases it was significantly less than zero. With the point of view described above, the recommended solution is to allow more scan time but at the present scan rate. With no other changes in the present configuration, the radar met design detection specifications for all design philosophies at a range of 11.3 nautical miles