Sampling statistics in the Atlantic menhaden fishery

Atlantic menhaden, Brrvoortia tyrannus, the object of a major purse-seine fishery along the U.S. east coast, are landed at plants from northern Florida to central Maine. The National Marine Fisheries Service has sampled these landings since 1955 for length, weight, and age. Together with records of landings at each plant, the samples are used to estimate numbers of fish landed at each age. This report analyzes the sampling design in terms of probablity sampling theory. The design is c1assified as two-stage cluster sampling, the first stage consisting of purse-seine sets randomly selected from the population of all sets landed, and the second stage consisting of fish randomly selected from each sampled set. Implicit assumptions of this design are discussed with special attention to current sampling procedures. Methods are developed for estimating mean fish weight, numbers of fish landed, and age composition of the catch, with approximate 95% confidence intervals. Based on specific results from three ports (port Monmouth, N.J., Reedville, Va., and Beaufort, N.C.) for the 1979 fishing season, recommendations are made for improving sampling procedures to comply more exactly with assumptions of the sampling design. These recommendatlons include adopting more formal methods for randomizing set and fish selection, increasing the number of sets sampled, considering the bias introduced by unequal set sizes, and developing methods to optimize the use of funds and personnel. (PDF file contains 22 pages.

An Overview of Spatial Econometrics

This paper offers an expository overview of the field of spatial econometrics. It first justifies the necessity of special statistical procedures for the analysis of spatial data and then proceeds to describe the fundamentals of these procedures. In particular, this paper covers three crucial techniques for building models with spatial data. First, we discuss how to create a spatial weights matrix based on the distances between each data point in a dataset. Next, we describe the conventional methods to formally detect spatial autocorrelation, both global and local. Finally, we outline the chief components of a spatial autoregressive model, noting the circumstances under which it would be appropriate to incorporate each component into a model. This paper seeks to offer a concise introduction to spatial econometrics that will be accessible to interested individuals with a background in statistics or econometrics

Computer program calculates monotonic maximum likelihood estimates using method of reversals

FORTRAN 2 computer program calculates maximum estimates of a monotonic non-decreasing response function. The program uses the method of reversals algorithm which applies to the analysis of univariate or multivariate sensitivity experiments

Corrections for a constant radial magnetic field in the g-2 and electric-dipole-moment experiments with muons in storage rings

We calculate the corrections for constant radial magnetic field in muon g--2 and electric-dipole-moment experiments in storage rings. While the correction is negligible for the current generation of g--2 experiments, it affects the upcoming muon electric-dipole-moment experiment at Fermilab.Comment: 8 page

Economic Growth and Recovery in the United States, 1919-1941

The first part of this chapter provides an overview of what lay behind record productivity growth in the US economy between 1929 and 1941. The second part considers the role of rigidities and other negative supply conditions in worsening the downturn and slowing recovery. While arguing consistently that the overarching explanation of the Great Depression will and should continue to emphasise a collapse and slow revival in the growth of aggregate demand, the chapter spends relatively little time on what drives this. The emphasis of the chapter is on aggregate supply—both the broad array of positive shocks that propelled potential and, eventually, actual output forward, and the negative conditions which, in interaction with aggregate demand, may have increased the size of the output gap and prolonged its persistence. An appendix offers discussion and updated calculations of productivity growth rates for the critical period 1929–41