The Finite-Sample Properties of Autoregressive Approximations of Fractionally-Integrated and Non-Invertible Processes

This paper investigates the empirical properties of autoregressive approximations to two classes of process for which the usual regularity conditions do not apply; namely the non-invertible and fractionally integrated processes considered in Poskitt (2006). In that paper the theoretical consequences of fitting long autoregressions under regularity conditions that allow for these two situations was considered, and convergence rates for the sample autocovariances and autoregressive coefficients established. We now consider the finite-sample properties of alternative estimators of the AR parameters of the approximating AR(h) process and corresponding estimates of the optimal approximating order h. The estimators considered include the Yule-Walker, Least Squares, and Burg estimators.Autoregression, autoregressive approximation, fractional process,

Higher-Order Improvements of the Sieve Bootstrap for Fractionally Integrated Processes

This paper investigates the accuracy of bootstrap-based inference in the case of long memory fractionally integrated processes. The re-sampling method is based on the semi-parametric sieve approach, whereby the dynamics in the process used to produce the bootstrap draws are captured by an autoregressive approximation. Application of the sieve method to data pre-filtered by a semi-parametric estimate of the long memory parameter is also explored. Higher-order improvements yielded by both forms of re-sampling are demonstrated using Edgeworth expansions for a broad class of statistics that includes first- and second-order moments, the discrete Fourier transform and regression coefficients. The methods are then applied to the problem of estimating the sampling distributions of the sample mean and of selected sample autocorrelation coefficients, in experimental settings. In the case of the sample mean, the pre-filtered version of the bootstrap is shown to avoid the distinct underestimation of the sampling variance of the mean which the raw sieve method demonstrates in finite samples, higher order accuracy of the latter notwithstanding. Pre-filtering also produces gains in terms of the accuracy with which the sampling distributions of the sample autocorrelations are reproduced, most notably in the part of the parameter space in which asymptotic normality does not obtain. Most importantly, the sieve bootstrap is shown to reproduce the (empirically infeasible) Edgeworth expansion of the sampling distribution of the autocorrelation coefficients, in the part of the parameter space in which the expansion is valid

Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap

This paper investigates the use of bootstrap-based bias correction of semi-parametric estimators of the long memory parameter in fractionally integrated processes. The re-sampling method involves the application of the sieve bootstrap to data pre-filtered by a preliminary semi-parametric estimate of the long memory parameter. Theoretical justification for using the bootstrap techniques to bias adjust log-periodogram and semi-parametric local Whittle estimators of the memory parameter is provided. Simulation evidence comparing the performance of the bootstrap bias correction with analytical bias correction techniques is also presented. The bootstrap method is shown to produce notable bias reductions, in particular when applied to an estimator for which analytical adjustments have already been used. The empirical coverage of confidence intervals based on the bias-adjusted estimators is very close to the nominal, for a reasonably large sample size, more so than for the comparable analytically adjusted estimators. The precision of inferences (as measured by interval length) is also greater when the bootstrap is used to bias correct rather than analytical adjustments.Comment: 38 page

Modification of the Douglas Neumann program to improve the efficiency of predicting component interference and high lift characteristics

The Douglas Neumann method for low-speed potential flow on arbitrary three-dimensional lifting bodies was modified by substituting the combined source and doublet surface paneling based on Green's identity for the original source panels. Numerical studies show improved accuracy and stability for thin lifting surfaces, permitting reduced panel number for high-lift devices and supercritical airfoil sections. The accuracy of flow in concave corners is improved. A method of airfoil section design for a given pressure distribution, based on Green's identity, was demonstrated. The program uses panels on the body surface with constant source strength and parabolic distribution of doublet strength, and a doublet sheet on the wake. The program is written for the CDC CYBER 175 computer. Results of calculations are presented for isolated bodies, wings, wing-body combinations, and internal flow

A study of the boundary flow in a rocket combustion chamber. Part 3 - Data report Final report

Rocket motor boundary flow sampling apparatu

The development of the DAST I remotely piloted research vehicle for flight testing an active flutter suppression control system

The development of the DAST I (drones for aerodynamic and structural testing) remotely piloted research vehicle is described. The DAST I is a highly modified BQM-34E/F Firebee II Supersonic Aerial Target incorporating a swept supercritical wing designed to flutter within the vehicle's flight envelope. The predicted flutter and rigid body characteristics are presented. A description of the analysis and design of an active flutter suppression control system (FSS) designed to increase the flutter boundary of the DAST wing (ARW-1) by a factor of 20% is given. The design and development of the digital remotely augmented primary flight control system and on-board analog backup control system is presented. An evaluation of the near real-time flight flutter testing methods is made by comparing results of five flutter testing techniques on simulated DAST I flutter data. The development of the DAST ARW-1 state variable model used to generate time histories of simulated accelerometer responses is presented. This model uses control surface commands and a Dryden model gust as inputs. The feasibility of the concept of extracting open loop flutter characteristics from closed loop FSS responses was examined. It was shown that open loop characteristics can be determined very well from closed loop subcritical responses

A study of the boundary flow in a rocket combustion chamber. Part 1 - Summary Final report

Boundary flow chemical composition and heat flux data from hydrazine-nitrogen tetroxide rocket engine - Part

A study of the boundary flow in a rocket combustion chamber. Part 4 - Development of experimental hardware and technique Final report

Boundary flow composition in rocket motor combustion chamber with exhaust system samplin

Subacute sclerosing panencephalitis, a measles complication, in an internationally adopted child.

A healthy 13-year-old boy who had spent the first 4.5 years of his life in an orphanage in Thailand before adoption by an American couple became ill with subacute sclerosing panencephalitis and died several months later. The boy had most likely contracted wild-type measles in Thailand. Measles complications are a risk in international adoptions