Contrary to its currently known characteristics, the nocturnal boundary layer over the Great Plains is frequently populated with a variety of turbulence-producing phenomena. C ASES-99 considers four scientific questions primarily related to the stable, nocturnal boundary layer, including the transition periods. The CASES-99 field program attempted to identify the sources and to quantify the physical characteristics of atmospheric phenomena occurring from the formative stages of the NBL 1 until its eventual breakup during the morning transition. The follow-up pro-1 Acronyms not defined in the text are defined in the appendix.

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Interest has been growing in testing for nonlinearity or chaos in economic data, but much controversy has arisen about the available results. This paper explores the reasons for these empirical difficulties. We designed and ran a single-blind controlled competition among five highly regarded tests for nonlinearity or chaos with ten simulated data series. The data generating mechanisms include linear processes, chaotic recursions, and nonchaotic stochastic processes; and both large and small samples were included in the experiment. The data series were produced in a single blind manner by the competition manager and sent by e-mail, without identifying information, to the experiment participants. Each such participant is an acknowledged expert in one of the tests and has a possible vested interest in producing the best possible results with that one test. The results of this competition provide much surprising information about the power functions of some of the best regarded tests for nonlinearity or noisy chaos.

We develop an ordinary least squares estimator of the long-memory parameter from a fractionally integrated process that is an alternative to the Geweke and Porter-Hudak (1983) estimator. Using the wavelet transform from a fractionally integrated process, we establish a log-linear relationship between the wavelet coecients' variance and the scaling parameter equal to the log-memory parameter. This log-linear relationship yields a consistent ordinary least squares estimator of the long-memory parameter when the wavelet coecients' population variance is replaced by their sample variance. We derive the small sample bias and variance of the ordinary least squares estimator and test it against the GPH estimator and the McCoy±Walden maximum likelihood wavelet estimator by conducting a number of Monte Carlo experiments. Based upon the criterion of choosing the estimator which minimizes the mean squared error, the wavelet OLS approach was superior to the GPH estimator, but inferior to the McCoy±Walden wavelet estimator for the processes simulated. However, given the simplicity of programming and running the wavelet OLS estimator and its statistical inference of the long-memory parameter we feel the general practitioner will be attracted to the wavelet OLS estimator.