Alkane Profiles in Tropical Forages

The role of n-alkanes in animal nutrition research has recently been reviewed by Dove and Mayes (1996). The measurement of voluntary intake (VI) using the naturally occurring odd chain length alkanes C31 or C33 in conjunction with administered even chain length alkanes such as C32 and C36 provides several advantages over the more conventional methods. Much of the development work involving this technology has been carried out with sheep or dairy cattle fed predominantly temperate pasture. Laredo et al., (1991) have published alkane profiles for a number of introduced tropical pasture grasses but no alkane profiles have been published for native tropical pasture grasses. Animal production for a consuming world : proceedings of 9th Congress of the Asian-Australasian Association of Animal Production Societies [AAAP] and 23rd Biennial Conference of the Australian Society of Animal Production [ASAP] and 17th Annual Symposium of the University of Sydney, Dairy Research Foundation, [DRF]. 2-7 July 2000, Sydney, Australia.

Elastic Constants of Sodium Nitrate from 77 to 300 K

The six independent elastic constants of sodium nitrate are determined using 10 MHz ultrasonic pulse echo superposition technique over the temperature interval 77 to 300 K. The values obtained at 300 K are C11 = 5.71, C12 = 2.16, C33 = 3.3, C13 = 1.66, C44 = 1.24, C14 = 0.82, and at 77 K C11 = 6.63, C12 = 2.07, C33 = 3.99, C13 = 1.67, C44 = 1.69, C14 = 1.16 all expressed in units of 1011 dyn/cm2.

Extract from plants

An extract of organic material from a species selected from the tribe Nicotianeae that comprises a mixture of C26 to C33 alkanes at a purity of at least 90% by volume of total extract.

Elastic constants of triclinic copper sulphate pentahydrate crystals

Using an iterative technique to obtain the exact solutions of the cubic Christoffel equation, the 21 elastic constants of copper sulphate pentahydrate have been determined at 25°C by the ultrasonic pulse echo method. The elastic constants, referred to the IRE recommended system of axes, are c11=5·65, c12=2·65, c13=3·21, c14=−0·33, c15=−0·08, c16=−0·39, c22=4·33, c23=3·47, c24=−0·07, c25=−0·21, c26=0·02, c33=5·69, c34=−0·44, c35=−0·21, c36=−0·16, c44=1·73, c45=0·09, c46=0·03, c55=1·22, c56=−0·26 and c66=1·00 in units of 1010 N m−2.

饱和链烷技术测定绵羊食性食量精确性研究

【目的】验证饱和链烷技术测定家畜食性和食量的精确性，确定绵羊粪便中链烷的回收率。【方法】于2004年秋季在中国科学院内蒙古草原生态系统定位研究站用不同放牧演替阶段的优势植物羊草（Lcymus chincnsis）、糙隐子草（Cleistogenes squarrosa）和冷蒿（Artemisia frigide）按一定比例混合组成日粮，饲喂9只2岁羯羊，每只羊投喂一粒QSM胶囊，试验期内每天记录绵羊实际牧草采食量、采食成分和排粪量，利用气相色谱分析牧草和粪样的链烷含量，应用链烷技术测定绵羊的排粪量、不同牧草呆食比例和总干物质采食量，并与实际值进行比较。【结果】3种牧草链烷模式存在种间差异；绵羊粪便中链烷的回收率随链烷长度的增加而线性增加；绵羊排粪量测定值与实际值存在极显著的正相关（P〈0．01，r=0．9994）；绵羊采食羊草、糙隐子草和冷蒿比例的测定值与实际值存在极显著（P〈0．01）正相关，相关系数分别为0．9913、0．9864和0．9999；绵羊干物质采食量用C33：C32和C31：C31比值测定的值分别比实际值低4％（±1．3％）和7％（±1．3％），但差异不显著（P〉0．05）。【结论】饱和链烷技术可以精确测定典型草原绵羊的排粪量、食物组成和采食量。

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.

Beyond Panel Unit Root Tests: Using Multiple Testing to Determine the Non Stationarity Properties of Individual Series in a Panel

Most panel unit root tests are designed to test the joint null hypothesis of a unit root for each individual series in a panel. After a rejection, it will often be of interest to identify which series can be deemed to be stationary and which series can be deemed nonstationary. Researchers will sometimes carry out this classification on the basis of n individual (univariate) unit root tests based on some ad hoc significance level. In this paper, we demonstrate how to use the false discovery rate (FDR) in evaluating I(1)=I(0) classifications based on individual unit root tests when the size of the cross section (n) and time series (T) dimensions are large. We report results from a simulation experiment and illustrate the methods on two data sets.

Anisotropy in elastic properties of lithium sodium sulphate hexahydrate single crystal—An ultrasonic study

The double sulfate family (ABSO4), where A and B are alkali metal cations, is the object of great interest owing to the complexity and richness of its sequence of phase transition induced by temperature variation. A new sulfate salt characterized by the presence of water molecule in the unit cell with the chemical formula, Li2Na3(SO4)2⋅6H2O (LSSW), was obtained. The ultrasonic velocity measurement was done with pulse echo overlap technique [PEO]. All the six second order elastic stiffness constants, C11 = C22, C33, C44 = C55, C12, C14 and C13 = C23 are reported for the first time. The anisotropy in the elastic properties of the crystal are well explained by the pictorial representation of the polar plots of phase velocity, slowness, Young’s modulus and linear compressibility in a–b and a–c planes.