INVERSE DEMAND RELATIONSHIPS FOR WHEAT FOOD USE BY CLASS

A normalized quadratic input distance system is applied to estimate inverse demand relationships for wheat by class. Semi-nonparametric and Bayesian estimators are used to impose curvature on inputs and outputs. Price flexibilities are estimated for hard red winter, hard red spring, soft red wheat, soft white winter, and durum wheat. Durum wheat is found to be the most price flexible. Economically and statistically important differences in price formation across classes of wheat are found and are supportive of government programs differentiating wheat by class.Demand and Price Analysis,

Input inefficiency in commercial banks: a normalized quadratic input distance approach

A normalized quadratic input distance function is proposed with which to estimate technical efficiency on commercial banks regulated by the Federal Reserve System. The study period covers 1990 to 2000 using individual bank information from the Call and Banking Holding Company Database. A stochastic frontier model is specified to estimate the input normalized distance function and obtain measures of technical efficiency.Banks and banking

IMPOSING CURVATURE RESTRICTIONS ON A TRANSLOG COST FUNCTION USING A MARKOV CHAIN MONTE CARLO SIMULATION APPROACH

Using Kansas Farm data from 1973 to 1998, curvature restrictions are imposed on a translog cost function. Using uninformative priors with indicator functions representing distribution and inequality constraints, a Markov Chain Monte Carlo Simulation method is used to estimate parameters and check curvature at each point. Comparison is made to the Cholesky factorization method commonly used with the normalized quadratic functional form.Research Methods/ Statistical Methods,

Multivariate AIM Consumer Demand Model Applied to Dried Fruit, Raisins, and Dried Plums

Abstract: We estimate a semi-nonparametric demand system based on a multivariate version of the Muntz-Szatz series expansion which is called the Asymptotically Ideal Model (AIM). The model is applied to consumer demand for dried fruits, raisins, and dried plums. Results from the first and second order AIM expansions suggest that the second order expansion leads to a more economically consistent model, but the likelihood ratio test indicates the AIM(2) model was not a statistical improvement over the AIM(1) model.demand, consumers, AIM, Demand and Price Analysis,

Empirical properties of duality theory

This research examines selected empirical properties of duality relationships. Monte Carlo experiments indicate that Hessian matrices estimated from the normalised unrestricted profit, restricted profit and production functions yield conflicting results in the presence of measurement error and low relative price variability. In particular, small amounts of measurement error in quantity variables can translate into large errors in uncompensated estimates calculated via restricted and unrestricted profit and production functions. These results emphasise the need for high quality data when estimating empirical models in order to accurately determine dual relationships implied by economic theory.Research Methods/ Statistical Methods,

SEM, EBSD, laser confocal microscopy and FE-SEM data from modern Glycymeris shell layers

GC acknowledges financial support of the 2017 Italian Ministry PRIN RX9XXY Project "Biota resilience to global change: biomineralization of planktic and benthic calcifiers in the past, present and future" to E. Erba. AGC is funded by project CGL2017-85118-P of the Spanish Ministerio de Ciencia e Innovacion, the Unidad Cientifica de Excelencia UCE-PP2016-05 of the University of Granada, and the Research Group RNM363 of the Junta de Andalucia.Here, we provide the dataset associated with the research article “Orientation patterns of aragonitic crossed-lamellar, fibrous prismatic and myostracal microstructures of modern Glycymeris shells” [1]. Based on several tools (SEM, EBSD, laser confocal microscopy and FE-SEM) we present original data relative to the microstructure and texture of aragonite crystallites in all Glycymeris shell layers (crossed-lamellar, complex crossed-lamellar, fibrous prismatic and pedal retractor and adductor myostraca) and address texture characteristics at the transition from one layer to the other, identifying similarities and differences among the different layers. Shells were cut transversely, obliquely and longitudinally in order to obtain different orientated sections of the outer and inner layer and of the myostraca. The identification of major microstructural elements was provided by detailed SEM and laser confocal microscopy images. Microstructure and texture characterization was based on EBSD measurements presented as band contrast images and as color-coded crystal orientation maps with corresponding pole figures. Crystal co-orientation was measured with the MUD value. Finally, the distribution of the organic matrix occluded within the outer crossed-lamellar layer was revealed using FE-SEM. These data, besides providing a modern unaltered Glycymeris reference to detect diagenetic alteration in fossil analogs used for paleoenvironmental reconstructions, are useful to better comprehend the mechanisms of bivalve shell formation.2017 Italian Ministry PRIN RX9XXY ProjectInstituto de Salud Carlos III Spanish Government CGL2017-85118-PUnidad Cientifica de Excelencia of the University of Granada UCE-PP2016-05Junta de Andalucia RNM36

Machine learning reduced workload for the Cochrane COVID-19 Study Register: development and evaluation of the Cochrane COVID-19 Study Classifier

BACKGROUND: This study developed, calibrated and evaluated a machine learning (ML) classifier designed to reduce study identification workload in maintaining the Cochrane COVID-19 Study Register (CCSR), a continuously updated register of COVID-19 research studies. METHODS: A ML classifier for retrieving COVID-19 research studies (the 'Cochrane COVID-19 Study Classifier') was developed using a data set of title-abstract records 'included' in, or 'excluded' from, the CCSR up to 18th October 2020, manually labelled by information and data curation specialists or the Cochrane Crowd. The classifier was then calibrated using a second data set of similar records 'included' in, or 'excluded' from, the CCSR between October 19 and December 2, 2020, aiming for 99% recall. Finally, the calibrated classifier was evaluated using a third data set of similar records 'included' in, or 'excluded' from, the CCSR between the 4th and 19th of January 2021. RESULTS: The Cochrane COVID-19 Study Classifier was trained using 59,513 records (20,878 of which were 'included' in the CCSR). A classification threshold was set using 16,123 calibration records (6005 of which were 'included' in the CCSR) and the classifier had a precision of 0.52 in this data set at the target threshold recall >0.99. The final, calibrated COVID-19 classifier correctly retrieved 2285 (98.9%) of 2310 eligible records but missed 25 (1%), with a precision of 0.638 and a net screening workload reduction of 24.1% (1113 records correctly excluded). CONCLUSIONS: The Cochrane COVID-19 Study Classifier reduces manual screening workload for identifying COVID-19 research studies, with a very low and acceptable risk of missing eligible studies. It is now deployed in the live study identification workflow for the Cochrane COVID-19 Study Register