Financial and economic performance of long-rotation hardwood plantation investments in Queensland, Australia

In Queensland, Australia, there is presently a high level of interest in long-rotation hardwood plantation investments for sawlog production, despite the consensus in Australian literature that such investments are not financially viable. Continuing genetics, silviculture and processing research, and increasing awareness about the ecosystem services generated by plantations, are anticipated to make future plantings profitable and socio-economically desirable in many parts of Queensland. Financial and economic models of hardwood plantations in Queensland are developed to test this hypothesis. The economic model accounts for carbon sequestration, salinity amelioration and other ecosystem service values of hardwood plantations. A carbon model estimates the value of carbon sequestered, while salinity and other ecosystem service values are estimated by the benefit transfer method. Where high growth rates (20-25 m(3) ha(-1) year(-1)) are achievable, long-rotation hardwood plantations are profitable in Queensland Hardwood Regions 1, 3 and 7 when rural land values are less than $2300/ha. Under optimistic assumptions, hardwood plantations growing at a rate of 15 in 3 ha-1 year 1 are financially viable in Hardwood Regions 2, 4 and 8, provided land values are less than $1600/ha. The major implication of the economic analysis is that long-rotation hardwood plantation forestry is socio-economically justified in most Hardwood Regions, even though financial returns from timber production may be negative. (c) 2003 Elsevier B.V. All rights reserved.

Ecological-economic modeling for biodiversity management: Potential, pitfalls, and prospects

Ecologists and economists both use models to help develop strategies for biodiversity management. The practical use of disciplinary models, however, can be limited because ecological models tend not to address the socioeconomic dimension of biodiversity management, whereas economic models tend to neglect the ecological dimension. Given these shortcomings of disciplinary models, there is a necessity to combine ecological and economic knowledge into ecological-economic models. It is insufficient if scientists work separately in their own disciplines and combine their knowledge only when it comes to formulating management recommendations. Such an approach does not capture feedback loops between the ecological and the socioeconomic systems. Furthermore, each discipline poses the management problem in its own way and comes up with its own most appropriate solution. These disciplinary solutions, however are likely to be so different that a combined solution considering aspects of both disciplines cannot be found. Preconditions for a successful model-based integration of ecology and economics include (1) an in-depth knowledge of the two disciplines, (2) the adequate identification and framing of the problem to be investigated, and (3) a common understanding between economists and ecologists of modeling and scale. To further advance ecological-economic modeling the development of common benchmarks, quality controls, and refereeing standards for ecological-economic models is desirable.

Forecasting stock returns using model-selection criteria

This paper examines the economic significance of return predictability in Australian equities. In light of considerable model uncertainty, formal model-selection criteria are used to choose a specification for the predictive model. A portfolio-switching strategy is implemented according to model predictions. Relative to a buy-and-hold market investment, the returns to the portfolio-switching strategy are impressive under several model-selection criteria, even after accounting for transaction costs. However, as these findings are not robust across other model-selection criteria examined, it is difficult to conclude that the degree of return predictability is economically significant.

Modelling knowledge production performance of research centres with a focus on triple bottom line benchmarking

We demonstrate a portable process for developing a triple bottom line model to measure the knowledge production performance of individual research centres. For the first time, this study also empirically illustrates how a fully units-invariant model of Data Envelopment Analysis (DEA) can be used to measure the relative efficiency of research centres by capturing the interaction amongst a common set of multiple inputs and outputs. This study is particularly timely given the increasing transparency required by governments and industries that fund research activities. The process highlights the links between organisational objectives, desired outcomes and outputs while the emerging performance model represents an executive managerial view. This study brings consistency to current measures that often rely on ratios and univariate analyses that are not otherwise conducive to relative performance analysis.

Estimating variable returns to scale production frontiers with alternative stochastic assumptions

Two stochastic production frontier models are formulated within the generalized production function framework popularized by Zellner and Revankar (Rev. Econ. Stud. 36 (1969) 241) and Zellner and Ryu (J. Appl. Econometrics 13 (1998) 101). This framework is convenient for parsimonious modeling of a production function with returns to scale specified as a function of output. Two alternatives for introducing the stochastic inefficiency term and the stochastic error are considered. In the first the errors are added to an equation of the form h(log y, theta) = log f (x, beta) where y denotes output, x is a vector of inputs and (theta, beta) are parameters. In the second the equation h(log y,theta) = log f(x, beta) is solved for log y to yield a solution of the form log y = g[theta, log f(x, beta)] and the errors are added to this equation. The latter alternative is novel, but it is needed to preserve the usual definition of firm efficiency. The two alternative stochastic assumptions are considered in conjunction with two returns to scale functions, making a total of four models that are considered. A Bayesian framework for estimating all four models is described. The techniques are applied to USDA state-level data on agricultural output and four inputs. Posterior distributions for all parameters, for firm efficiencies and for the efficiency rankings of firms are obtained. The sensitivity of the results to the returns to scale specification and to the stochastic specification is examined. (c) 2004 Elsevier B.V. All rights reserved.

A machine learning approach to intraday trading on foreign exchange markets

Foreign Exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. In this paper we try to create such a system using Machine learning approach to emulate trader behaviour on the Foreign Exchange market and to find the most profitable trading strategy.

Forecasting with serially correlated regression models

In this article we investigate the asymptotic and finite-sample properties of predictors of regression models with autocorrelated errors. We prove new theorems associated with the predictive efficiency of generalized least squares (GLS) and incorrectly structured GLS predictors. We also establish the form associated with their predictive mean squared errors as well as the magnitude of these errors relative to each other and to those generated from the ordinary least squares (OLS) predictor. A large simulation study is used to evaluate the finite-sample performance of forecasts generated from models using different corrections for the serial correlation.

The specification of vector autoregressive moving average models

In this paper we propose a new identification method based on the residual white noise autoregressive criterion (Pukkila et al. , 1990) to select the order of VARMA structures. Results from extensive simulation experiments based on different model structures with varying number of observations and number of component series are used to demonstrate the performance of this new procedure. We also use economic and business data to compare the model structures selected by this order selection method with those identified in other published studies.

Economic competition and evolution: Are there lessons from ecology?

After generally discussing models in ecology and economics that combine competition, optimization, and evolution, this article concentrates on models of intraspecific competition. It demonstrates the importance of diversity/inequalities within populations of species and other environments for the sustainability of their populations, given the occurrence of environmental change. This is demonstrated both for scramble (open-access) and contest competition. Implications are drawn for human populations and industrial organization. The possibility is raised that within-industry competition may not always exist between firms in all stages of the development of a new industry. Policy implications are considered. For example, it is argued that policies designed to encourage intense business competition and maximum economic efficiency have the drawback of eventually making industries highly vulnerable to exogenous economic changes.

Do open-cycle hatcheries relying on tourism conserve sea turtles? Sri Lankan developments and economic-ecological considerations

By combining economic analysis of markets with ecological parameters, this article considers the role that tourism-based sea turtle hatcheries (of an open-cycle type) can play in conserving populations of sea turtles. Background is provided on the nature and development of such hatcheries in Sri Lanka. The modeling facilitates the assessment of the impacts of turtle hatcheries on the conservation of sea turtles and enables the economic and ecological consequences of tourism, based on such hatcheries, to be better appreciated. The results demonstrate that sea turtle hatcheries serving tourists can make a positive contribution to sea turtle conservation, but that their conservation effectiveness depends on the way they are managed. Possible negative effects are also identified. Economic market models are combined with turtle population survival relationships to predict the conservation impact of turtle hatcheries and their consequence for the total economic value obtained from sea turtle populations.

What determines the finance-growth nexus? Empirical evidence for threshold models

This paper elaborates the notion of balanced'' financial development that is contingent on a country's general level of development. We develop an empirical framework to address this point, referring to threshold regressions and a bootstrap test for structural shift in a growth equation. We find that countries gain less from financial activity, if the latter fails to keep up with or exceeds what would follow from a balanced expansion path. These analyses contribute to the finance and growth literature in providing empirical support for the balanced'' financial development hypothesis.

Accounting for management costs in sensitivity analyses of matrix population models

Traditional sensitivity and elasticity analyses of matrix population models have been used to p inform management decisions, but they ignore the economic costs of manipulating vital rates. For exam le, the growth rate of a population is often most sensitive to changes in adult survival rate, but this does not mean that increasing that rate is the best option for managing the population because it may be much more expensive than other options. To explore how managers should optimize their manipulation of vital rates, we incorporated the cost of changing those rates into matrix population models. We derived analytic expressions for locations in parameter space where managers should shift between management of fecundity and survival, for the balance between fecundity and survival management at those boundaries, and for the allocation of management resources to sustain that optimal balance. For simple matrices, the optimal budget allocation can often be expressed as simple functions of vital rates and the relative costs of changing them. We applied our method to management of the Helmeted Honeyeater (Lichenostomus melanops cassidix; an endangered Australian bird) and the koala (Phascolarctos cinereus) as examples. Our method showed that cost-efficient management of the Helmeted Honeyeater should focus on increasing fecundity via nest protection, whereas optimal koala management should focus on manipulating both fecundity and survival simultaneously, These findings are contrary to the cost-negligent recommendations of elasticity analysis, which would suggest focusing on managing survival in both cases. A further investigation of Helmeted Honeyeater management options, based on an individual-based model incorporating density dependence, spatial structure, and environmental stochasticity, confirmed that fecundity management was the most cost-effective strategy. Our results demonstrate that decisions that ignore economic factors will reduce management efficiency.