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There are no direct methods to evaluate calculated soil heat flux (SHF) at the surface (G0). Instead, validation and cross evaluation of methods for calculating G0usually rely on the conventional calorimetric method or the degree of the surface energy balance closure. However, there is uncertainty in the calorimetric method itself, and factors apart from G0also contribute to nonclosure of the surface energy balance. Here we used a novel approach to evaluate nine different methods for calculating SHF, including the calorimetric method and methods based on analytical solutions of the heat diffusion equation. The SHF (Gz) measured by a self‐calibrating SHF plate at a depth of z= 5 cm below the surface (hereafter Gm_5cm) was deployed as a reference. Each SHF calculation method was assessed by comparing the calculated Gzat the same depth (hereafter Gc_5cm) with Gm_5cm. The calorimetric method and simple measurement method performed best in determining Gc_5cmbut still underestimated Gm_5cmby 19% during the daytime. Possible causes for this underestimation include errors and uncertainties in SHF measurements and soil thermal properties, as well as the phase lag between Gc_5cmand Gm_5cm. Our results indicate that the calorimetric method achieves the most accurate SHF estimates if self‐calibrating SHF plates are deployed at two depths (e.g., 5 cm and 10 cm), soil temperature and water content measurements are made in a few depths between the two plates, and soil thermal properties are accurately quantified. We evaluated nine methods of soil heat flux calculation using field observationsAll nine methods underestimated the soil heat flux by at least 19%This large underestimation is mainly caused by uncertainties in soil thermal properties

Occupiers of office buildings are seeking higher levels of flexibility, while property owners are more interested in stability. The British Council for Offices is addressing this problem through a working party uniting owners, occupiers, serviced office providers, developers and professional advisors.

Abstract: A Bayesian Markov chain Monte Carlo methodology is developed for the estimation of multivariate linear Gaussian state space models. In particular, an efficient simulation smoothing algorithm is proposed that makes use of the univariate representation of the state space model. Substantial gains over existing algorithms in computational efficiency are achieved using the new simulation smoother for the analysis of high dimensional multivariate time series. The methodology is used to analyse a multivariate time series dataset of the Normalised Difference Vegetation Index (NDVI), which is a proxy for the level of live vegetation, for a particular grazing property located in Queensland, Australia. [Copyright &y& Elsevier]

Abstract: The impact of parameterisation on the simulation efficiency of Bayesian Markov chain Monte Carlo (MCMC) algorithms for two non-Gaussian state space models is examined. Specifically, focus is given to particular forms of the stochastic conditional duration (SCD) model and the stochastic volatility (SV) model, with four alternative parameterisations of each model considered. A controlled experiment using simulated data reveals that relationships exist between the simulation efficiency of the MCMC sampler, the magnitudes of the population parameters and the particular parameterisation of the state space model. Results of an empirical analysis of two separate transaction data sets for the SCD model, as well as equity and exchange rate data sets for the SV model, are also reported. Both the simulation and empirical results reveal that substantial gains in simulation efficiency can be obtained from simple reparameterisations of both types of non-Gaussian state space models. [Copyright &y& Elsevier]

Abstract: A Bayesian Markov chain Monte Carlo methodology is developed for estimating the stochastic conditional duration model. The conditional mean of durations between trades is modelled as a latent stochastic process, with the conditional distribution of durations having positive support. Regressors are included in the model for the latent process in order to allow additional variables to impact on durations. The sampling scheme employed is a hybrid of the Gibbs and Metropolis-Hastings algorithms, with the latent vector sampled in blocks. Candidate draws for the latent process are generated by applying a Kalman filtering and smoothing algorithm to a linear Gaussian approximation of the non-Gaussian state space representation of the model. Monte Carlo sampling experiments demonstrate that the Bayesian method performs better overall than an alternative quasi-maximum likelihood approach. The methodology is illustrated using Australian intraday stock market data, with Bayes factors used to discriminate between different distributional assumptions for durations. [Copyright &y& Elsevier]

Part III. Discusses a model in pricing energy derivatives in trees. Simplified version of the preferred multi-factor model; Valuation of options.

Focuses on the pricing of energy derivatives in the United States. Evolution of energy prices; Trading of cap agreement; Monthly caps on natural gas.