5,158 research outputs found

    Bootstrapping Cointegrating Regressions

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    In this paper, we consider bootstrapping cointegrating regressions. It is shown that the method of bootstrap, if properly implemented, generally yields consistent estimators and test statistics for cointegrating regressions. We do not assume any specific data generating process, and employ the sieve bootstrap based on the approximated finite-order vector autoregressions for the regression errors and the firrst differences of the regressors. In particular, we establish the bootstrap consistency for OLS method. The bootstrap method can thus be used to correct for the finite sample bias of the OLS estimator and to approximate the asymptotic critical values of the OLS-based test statistics in general cointegrating regressions. The bootstrap OLS procedure, however, is not efficient. For the efficient estimation and hypothesis testing, we consider the procedure proposed by Saikkonen (1991) and Stock and Watson (1993) relying on the regression augmented with the leads and lags of differenced regressors. The bootstrap versions of their procedures are shown to be consistent, and can be used to do inferences that are asymptotically valid. A Monte Carlo study is conducted to investigate the finite sample performances of the proposed bootstrap methods.

    SUPERPOSITION PRINCIPLE APPLIED TO THE OPTIMIZATION OF KICK-TO-STROKE RATIO OF BACKSTROKE

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    Backstrokers acknowledge there is an apparent rhythm in swimming that is characterised by the periodic motion of the arms and legs, which must be optimally sychnronized for effective performance. However, unlike the freestyle crawl where the optimal kick-stroke ratio is established at 3:1, there is a limited understanding of the optimal ratio for backstrokers. By separating the velocity contributions of the kick and stroke, the superposition principle can be utilised to find the optimal kick-stroke ratio to maximize swimming distance, the area below the superimposed velocity curve. This paper intends to mathematically establish this ratio and thus improve the performance of backstrokers

    AN ANALYSIS OF FACTORS ASSOCIATED WITH COMPOSTING BEHAVIOR AT THE HOUSEHOLD LEVEL

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    Drawing upon telephone survey data, a logit probability analysis was conducted to identify household characteristics as well as social and institutional factors associated with backyard composting of yard and food wastes. Highly significant predictors included household gardening, perception of effort required, peer influence, and a compost bin sale program.Consumer/Household Economics, Institutional and Behavioral Economics,

    MCMCpack: Markov Chain Monte Carlo in R

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    We introduce MCMCpack, an R package that contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. In addition to code that can be used to fit commonly used models, MCMCpack also contains some useful utility functions, including some additional density functions and pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization.

    Factors Associated with Backyard Composting Behavior at the Household Level

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    Communities in most states are under pressure to reduce the amount of solid waste going into landfills. Many are making efforts to encourage their citizens to practice backyard composting. A logit regression analysis was conducted to identify factors associated with backyard composting of yard and food wastes in a case study area. Sample data were obtained through a September 1997 telephone survey of 865 households residing in single-family dwellings in Knox County, Tennessee. Findings indicate that a number of variables reflecting complementary behavior, attitudes, knowledge, and peer influence were significantly related to composting behavior. Policy implications of these findings are outlined.Resource /Energy Economics and Policy,

    Analysis of dependence among size, rate and duration in internet flows

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    In this paper we examine rigorously the evidence for dependence among data size, transfer rate and duration in Internet flows. We emphasize two statistical approaches for studying dependence, including Pearson's correlation coefficient and the extremal dependence analysis method. We apply these methods to large data sets of packet traces from three networks. Our major results show that Pearson's correlation coefficients between size and duration are much smaller than one might expect. We also find that correlation coefficients between size and rate are generally small and can be strongly affected by applying thresholds to size or duration. Based on Transmission Control Protocol connection startup mechanisms, we argue that thresholds on size should be more useful than thresholds on duration in the analysis of correlations. Using extremal dependence analysis, we draw a similar conclusion, finding remarkable independence for extremal values of size and rate.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS268 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Allons

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    The purpose of this project was to create a functional yet stylish outfit for plus-sized individuals, appropriate for outdoor activities. The clothes are intended to promote an active lifestyle. The outfit was designed around the fabric choice of cotton and jersey knit to provide the wearer with functional comfort, while offering the desired casual look
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