Parameter estimation using a particle method: inferring mixing coefficients from sea level observations

This paper presents a first attempt to estimate mixing parameters from sea level observations using a particle method based on importance sampling. The method is applied to an ensemble of 128 members of model simulations with a global ocean general circulation model of high complexity. Idealized twin experiments demonstrate that the method is able to accurately reconstruct mixing parameters from an observed mean sea level field when mixing is assumed to be spatially homogeneous. An experiment with inhomogeneous eddy coefficients fails because of the limited ensemble size. This is overcome by the introduction of local weighting, which is able to capture spatial variations in mixing qualitatively. As the sensitivity of sea level for variations in mixing is higher for low values of mixing coefficients, the method works relatively well in regions of low eddy activity.

Calibration and use of a cesium-iodide prism in the infrared

With a cesium-iodide prism the long wavelength range of an infrared spectrometer may be extended to 55µ The use of such a prism, the choice of optical system, and the problems of stray radiation are all discussed. Accurate data are assembled for calibration in this region, and sample calibration traces are shown. A simple gas absorption cell is described for use at long wavelengths.

Bayesian estimation of co-integrating thresholds in the term structure of interest rates.

Threshold Error Correction Models are used to analyse the term structure of interest Rates. The paper develops and uses a generalisation of existing models that encompasses both the Band and Equilibrium threshold models of [Balke and Fomby ((1997) Threshold cointegration. Int Econ Rev 38(3):627–645)] and estimates this model using a Bayesian approach. Evidence is found for threshold effects in pairs of longer rates but not in pairs of short rates. The Band threshold model is supported in preference to the Equilibrium model.

Bayesian estimation of willingness-to-pay where respondents mis-report their preferences

We introduce a modified conditional logit model that takes account of uncertainty associated with mis-reporting in revealed preference experiments estimating willingness-to-pay (WTP). Like Hausman et al. [Journal of Econometrics (1988) Vol. 87, pp. 239-269], our model captures the extent and direction of uncertainty by respondents. Using a Bayesian methodology, we apply our model to a choice modelling (CM) data set examining UK consumer preferences for non-pesticide food. We compare the results of our model with the Hausman model. WTP estimates are produced for different groups of consumers and we find that modified estimates of WTP, that take account of mis-reporting, are substantially revised downwards. We find a significant proportion of respondents mis-reporting in favour of the non-pesticide option. Finally, with this data set, Bayes factors suggest that our model is preferred to the Hausman model.

Model selection for the mixed logit with Bayesian estimation

In this paper, the mixed logit (ML) using Bayesian methods was employed to examine willingness-to-pay (WTP) to consume bread produced with reduced levels of pesticides so as to ameliorate environmental quality, from data generated by a choice experiment. Model comparison used the marginal likelihood, which is preferable for Bayesian model comparison and testing. Models containing constant and random parameters for a number of distributions were considered, along with models in ‘preference space’ and ‘WTP space’ as well as those allowing for misreporting. We found: strong support for the ML estimated in WTP space; little support for fixing the price coefficient a common practice advocated and adopted in the environmental economics literature; and, weak evidence for misreporting.

Bayesian estimation and selection of nonlinear vector error correction models: The case of the sugar-ethanol-oil nexus in Brazil

Nonlinear adjustment toward long-run price equilibrium relationships in the sugar-ethanol-oil nexus in Brazil is examined. We develop generalized bivariate error correction models that allow for cointegration between sugar, ethanol, and oil prices, where dynamic adjustments are potentially nonlinear functions of the disequilibrium errors. A range of models are estimated using Bayesian Monte Carlo Markov Chain algorithms and compared using Bayesian model selection methods. The results suggest that the long-run drivers of Brazilian sugar prices are oil prices and that there are nonlinearities in the adjustment processes of sugar and ethanol prices to oil price but linear adjustment between ethanol and sugar prices.

Estimation of the stoichiometry of volatile fatty acid production in the rumen of lactating cows

The purpose of this study was to improve the prediction of the quantity and type of Volatile Fatty Acids (VFA) produced from fermented substrate in the rumen of lactating cows. A model was formulated that describes the conversion of substrate (soluble carbohydrates, starch, hemi-cellulose, cellulose, and protein) into VFA (acetate, propionate, butyrate, and other VFA). Inputs to the model were observed rates of true rumen digestion of substrates, whereas outputs were observed molar proportions of VFA in rumen fluid. A literature survey generated data of 182 diets (96 roughage and 86 concentrate diets). Coefficient values that define the conversion of a specific substrate into VFA were estimated meta-analytically by regression of the model against observed VFA molar proportions using non-linear regression techniques. Coefficient estimates significantly differed for acetate and propionate production in particular, between different types of substrate and between roughage and concentrate diets. Deviations of fitted from observed VFA molar proportions could be attributed to random error for 100%. In addition to regression against observed data, simulation studies were performed to investigate the potential of the estimation method. Fitted coefficient estimates from simulated data sets appeared accurate, as well as fitted rates of VFA production, although the model accounted for only a small fraction (maximally 45%) of the variation in VFA molar proportions. The simulation results showed that the latter result was merely a consequence of the statistical analysis chosen and should not be interpreted as an indication of inaccuracy of coefficient estimates. Deviations between fitted and observed values corresponded to those obtained in simulations. (c) 2005 Elsevier Ltd. All rights reserved.

Estimation of milk production from smallholder dairy cattle in the coastal lowlands of Kenya

A study was carried out on 92 smallholder farms in Kwale district in Coast Province of Kenya to estimate the milk yield. The effect of concentrate feed supplementation on milk yield was also evaluated. Data were collected during a one-year observational longitudinal study. Analysis was done for 371 observations following 63 calving events. The mean annual milk offtake was estimated at 2021 kg/cow. Forty-nine (77.8%) of the lactating cows were supplemented with concentrate feeds at varying rates of 0.5-3.0 kg/cow per day. Supplementary feeding of lactating cows led to a significantly higher mean daily milk yield compared to non-supplemented cows throughout the year (p<0.05). The mean annual milk offtake from supplemented cows (2195 kg/cow) was 18.6% more than offtake from non-supplemented cows, a difference that was statistically significant (p<0.05). Therefore, supplementary feeding of commercial feed concentrates was a rational management practice. It was also concluded that milk production from smallholder dairy cows in the coastal lowlands of Kenya was comparable to that from similar production systems but lower than national targets.

Seroprevalence estimation and risk factors for A. marginale on smallholder dairy farms in Tanzania

A cross-sectional serological survey of A. marginale was conducted on 200 randomly selected smallholder farms in each of the Tanga and Iringa Regions of Tanzania between January and April 1999. Sera, from dairy cattle of all ages, sexes and breeds were tested for antibodies against A. marginale using an indirect enzyme-linked immunosorbent assay. Antibodies to A. marginale were present in cattle throughout the study areas and the overall prevalence was 20% for Tanga and 37% for Iringa. The forces of infection based on the age seroprevalence profile were estimated at 8 for Tanga and 15 for Iringa per 100 cattle years-risk, respectively. In both regions, seroprevalence increased with age (β = 0.01 and 0.017 per year of age, p < 0.005, in Tanga and Iringa, respectively). Older animals in Iringa were significantly and negatively associated with decreased seropositivity (β = −0.002, p = 0.0029). Further results of logistic regression models reveal that geographic location of animals in Tanga was associated with seropositivity (odds ratio (OR) = 2.94, p = 0.005, for Tanga Rural and OR = 2.38, p = 0.066, for Muheza). Animals acquired as a gift in Iringa had higher odds for seropositivity than brought-in cattle (OR = 2.44, p = 0.005). Our study has identified and quantified some key risk factors that can guide planners devising disease control strategies.

Cross-sectional estimation of Babesia bovis antibody prevalence in cattle in two contrasting dairying areas in Tanzania

The crude prevalence of antibodies to Babesia bovis infection in cattle was estimated by serology using indirect ELISA during the period January to April, 1999. Sera were obtained from 1395 dairy cattle (of all ages, sexes and breeds) on smallholder farms, the majority being kept under a zero grazing regime. The crude prevalence of antibodies to Babesia bovis was 6 % for Tanga and 12 % for Iringa. The forces of infection based on the age sero-prevalence profile, were estimated at six for Iringa and four for Tanga per 100 cattle years-risk, respectively. Using random effect logistic regression as the analytical method, the factors (variables) of age, source of animals and geographic location were hypothesised to be associated with sero-positivity of Babesia bovis in the two regions.

Seroprevalence estimation and risk factors for A-marginale on smallholder dairy farms in Tanzania

A cross-sectional serological survey of A. marginale was conducted on 200 randomly selected smallholder farms in each of the Tanga and Iringa Regions of Tanzania between January and April 1999. Sera, from dairy cattle of all ages, sexes and breeds were tested for antibodies against A. marginale using an indirect enzyme-linked immunosorbent assay. Antibodies to A. marginale were present in cattle throughout the study areas and the overall prevalence was 20% for Tanga and 37% for Iringa. The forces of infection based on the age seroprevalence profile were estimated at 8 for Tanga and 15 for Iringa per 100 cattle years-risk, respectively. In both regions, seroprevalence increased with age (beta = 0.01 and 0.017 per year of age, p < 0.005, in Tanga and Iringa, respectively). Older animals in Iringa were significantly and negatively associated with decreased seropositivity (beta = -0.002, p = 0.0029). Further results of logistic regression models reveal that geographic location of animals in Tanga was associated with seropositivity (odds ratio (OR) = 2.94, p = 0.005, for Tanga Rural and OR = 2.38, p = 0.066, for Muheza). Animals acquired as a gift in Iringa had higher odds for seropositivity than brought-in cattle (OR = 2.44, p = 0.005). Our study has identified and quantified some key risk factors that can guide planners devising disease control strategies.

Testing symmetry and homogeneity in the almost ideal demand system with co-integrated data using fully modified estimation and the bootstrap

Conventional seemingly unrelated estimation of the almost ideal demand system is shown to lead to small sample bias and distortions in the size of a Wald test for symmetry and homogeneity when the data are co-integrated. A fully modified estimator is developed in an attempt to remedy these problems. It is shown that this estimator reduces the small sample bias but fails to eliminate the size distortion.. Bootstrapping is shown to be ineffective as a method of removing small sample bias in both the conventional and fully modified estimators. Bootstrapping is effective, however, as a method of removing. size distortion and performs equally well in this respect with both estimators.