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on Computational Economics |
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Issue of 2006‒06‒03
five papers chosen by |
| By: | Maria Julia Seixas; João Pedro Nunes; Pedro Lourenço; Fernando Lobo; Paulo Condado |
| Abstract: | Future land use configurations provide valuable knowledge for policy makers and economic agents, especially under expected environmental changes such as decreasing rainfall or increasing temperatures, or scenarios of policy guidance such as carbon sequestration enforcement. In this paper, modelling land use change is designed as an optimization problem in which landscapes (land uses) are generated through the use of genetic algorithms (GA), according to an objective function (e.g. minimization of soil erosion, or maximization of carbon sequestration), and a set of local restrictions (e.g. soil depth, water availability, or landscape structure). GAs are search and optimization procedures based on the mechanics of natural selection and genetics. The GA starts with a population of random individuals, each corresponding to a particular candidate solution to the problem. The best solutions are propagated; they are mated with each other and originate “offspring solutions” which randomly combine the characteristics of each “parent”. The repeated application of these operations leads to a dynamic system that emulates the evolutionary mechanisms that occur in nature. The fittest individuals survive and propagate their traits to future generations, while unfit individuals have a tendency to die and become extinct (Goldberg, 1989). Applications of GA to land use planning have been experimented (Brookes, 2001, Ducheyne et al, 2001). However, long-term planning with a time-span component has not yet been addressed. GeneticLand, the GA for land use generation, works on a region represented by a bi-dimensional array of cells. For each cell, there is a number of possible land uses (U1, U2, ..., Un). The task of the GA is to search for an optimal assignment of these land uses to the cells, evolving the landscape patterns that are most suitable for satisfying the objective function, for a certain time period (e.g. 50 years in the future). GeneticLand develops under a multi-objective function: (i) Minimization of soil erosion – each solution is validated by applying the USLE, with the best solution being the one that minimizes the landscape soil erosion value; (ii) Maximization of carbon sequestration – each solution is validated by applying atmospheric CO2 carbon uptake estimates, with the best solution being the one that maximizes the landscape carbon uptake; and (iii) Maximization of the landscape economic value – each solution is validated by applying an economic value (derived from expert judgment), with the best solution being the one that maximizes the landscape economic value. As an optimization problem, not all possible land use assignments are feasible. GeneticLand considers two sets of restrictions that must be met: (i) physical constraints (soil type suitability, slope, rainfall-evapotranspiration ratio, and a soil wetness index) and (ii) landscape ecology restrictions at several levels (minimum patch area, land use adjacency index and landscape contagion index). The former assures physical feasibility and the latter the spatial coherence of the landscape. The physical and landscape restrictions were derived from the analysis of past events based on a time series of Landsat images (1985-2003), in order to identify the drivers of land use change and structure. Since the problem has multiple objectives, the GA integrates multi-objective extensions allowing it to evolve a set of non-dominated solutions. An evolutive type algorithm – Evolutive strategy (1+1) – is used, due to the need to accommodate the very large solution space. Current applications have about 1000 decision variables, while the problem analysed by GeneticLand has almost 111000, generated by a landscape with 333*333 discrete pixels. GeneticLand is developed and validated for a Mediterranean type landscape located in southern Portugal. Future climate triggers, such as the increase of intense rainfall episodes, is accommodated to simulate climate change . This paper presents: (1) the formulation of land use modelling as an optimization problem; (2) the formulation of the GA for the explicit spatial domain, (3) the land use constraints derived for a Mediterranean landscape, (4) the results illustrating conflicting objectives, and (5) limitations encountered. |
| Date: | 2005–08 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wiwrsa:ersa05p753&r=cmp |
| By: | Møller, Peder Fredslund (Department of Accounting, Aarhus School of Business) |
| Abstract: | This paper shows that settlement-date accounting for equity share options can be seen as an accounting method which implements a shareholder focused residually rewarded partners’ equity view. This equity view represents a simple, natural extension of the shareholder proprietary view. It implicates an equity and income sharing model for accounting which is characterized by specification of both shareholders’ and non-shareholders’ parts of total equity and income. When using this equity and income sharing model, the remeasurements of equity share option obligations made by settlement-date accounting are fully conceptually valid. They represent measurements of one partner group’s share of total equity with effect for another group’s share of total equity and income: the shareholders’ part. Partially, this equity and income sharing model is already the basis for existing accounting standards. <p> It is shown that an intriguing implication of the equity and income sharing model is the fact that treasury shares can hedge present shareholders’ share price risk from the obligation to holders of equity share options. A special hedge accounting construct is needed to account for this hedge effect, and the construct of this model is shown. Numerical simulations are used to illustrate the long run expense effects for shareholders from equity share options by settlement-date accounting both when the expense effects are unhedged and when they are hedged with treasury share holdings. The results demonstrate that the expenses resulting from settlement-date accounting for equity share option awards are significantly higher on average than the expenses resulting from grant-date accounting. And they show that the cost of equity, the share price volatility and the lifetime of the equity share options are important determinants for the size of the differences in total expenses, which in a long run perspective is to be expected from the use of these two alternative accounting models for equity share options. The simulation results demonstrate that hedging with treasury share holdings is very effective to stabilize expenses resulting from options granted to employees |
| Keywords: | No keywords; |
| Date: | 2006–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:hhb:aaracc:90-002&r=cmp |
| By: | Staszewska, Anna; Aldrich, John |
| Abstract: | This paper examines the experiment in macroeconometrics, the different forms it has taken and the rules that have been proposed for its proper conduct. Here an "experiment" means putting a question to a model and getting an answer. Different types of experiment are distinguished and the justification that can be provided for a particular choice of experiment is discussed. Three types of macroeconometric modelling are considered: the Cowles (system of equations) approach, the vector autoregressive model approach and the computational experiment. Keywords; experiment, impulse response analysis, ceteris paribus, structural invariance JEL classification: B41, C5, E37 |
| URL: | http://d.repec.org/n?u=RePEc:stn:sotoec:0604&r=cmp |
| By: | Susumu Shikano (University of Mannheim) |
| Abstract: | Recently, Bayesian methods such as Markov chain Monte Carlo (MCMC) techniques have found more use in the social sciences, with (Win)BUGS being one of the most widely applied programs for this kind of analysis. Unfortunately, because of the absence of MCMC techniques and any interfaces to WinBUGS or BUGS in Stata, Stata users who apply MCMC techniques have to perform such painful tasks as reformatting data themselves. As a preliminary solution to this problem, one can call another statistical software R within Stata and use it as an interface to (Win)BUGS. This presentation outlines this solution, providing a thorough analysis. |
| Date: | 2006–05–24 |
| URL: | http://d.repec.org/n?u=RePEc:boc:dsug06:09&r=cmp |
| By: | Ernst Juerg Weber (Department of Economics, The University of Western Australia) |
| Abstract: | Dynamic optimization is widely used in financial economics, macroeconomics and resource economics. This is accounting for some tension between the undergraduate and graduate teaching of economics because most undergraduate programs still concentrate on static economic analysis. This paper shows how, with the help of the Microsoft Excel Solver tool, the principles of dynamic economics can be taught to students with minimal knowledge of calculus. As it is assumed that the reader has no prior knowledge of optimal control theory, some attention is paid to the main concepts of dynamic optimization. |
| Keywords: | Optimal Control Theory, Economic Education, Microsoft Excel |
| JEL: | A22 C61 D91 D92 Q00 |
| Date: | 2005–01 |
| URL: | http://d.repec.org/n?u=RePEc:uwa:wpaper:05-07&r=cmp |