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/ This paper assesses the changing role of dynamic modeling for understanding and managing complex ecological economic systems. It discusses new modeling tools for problem scoping and consensus building among a broad range of stakeholders and describes four case studies in which dynamic modeling has been used to collect and organize data, synthesize knowledge, and build consensus about the management of complex systems. The case studies range from industrial systems (mining, smelting, and refining of iron and steel in the United States) to ecosystems (Louisiana coastal wetlands, and Fynbos ecosystems in South Africa) to linked ecological economic systems (Maryland's Patuxent River basin in the United States). They illustrate uses of dynamic modeling to include stakeholders in all stages of consensus building, ranging from initial problem scoping to model development. The resultant models are the first stage in a three-stage modeling process that includes research and management models as the later stages.KEY WORDS: Dynamic modeling; Scoping; Consensus building; Environmental management; Ecosystem management; Policy making; Graphical programming languages

En este trabajo se evalúa el desempeño de tres modelos dinámicos de la estructura a plazos de tasas de interés para estimar el valor en riesgo (VaR, por su traducción de Value at Risk) de portafolios de renta fija. De esta forma, se encuentra que el modelo de Diebold, Rudebusch y Aruoba se desempeña adecuadamente respecto a las pruebas de backtesting del VaR, mientras que el modelo de Diebold y Li y un modelo afín de no arbitraje exhiben un pobre desempeño. Los tres modelos asumen que la matriz de varianzas y covarianzas de los factores latentes a cada modelo es constante, lo cual limita su utilidad en el cálculo del VaR. Por lo tanto, modelos que relajen este supuesto deberían ofrecer un mejor desempeño y ser más adecuados para la gestión del riesgo de portafolios de renta fija. In this article we assess the performance of three interest rate dynamic term structure models in order to estimate the Value at Risk (VaR) of fixed-income portfolios. We find that that the model proposed by Diebold, Rudebusch and Aruoba performs appropriately in VaR backtesting statistical tests, while the model from Diebold and Li and a no-arbitrage akin term structure model display serious problems. The three models assume that the variance-covariance matrix for their underlying factors is constant, which limits their usefulness in estimating the VaR. Therefore, those models that relax this assumption should perform better and be more adequate for risk-management of fixed-income portfolios. Neste artigo, o desempenho de três modelos dinâmicos da estrutura a prazos das taxas de juros para estimar o valor em risco (VaR, por sua tradução de Value at Risk) de carteiras de renda fixa é avaliado. Assim, verificou-se que o modelo de Diebold, Rudebusch e Aruoba funciona adequadamente respeitar no que se refere ao backtesting do VaR; enquanto o modelo de Diebold e Li e um modelo relacionado de não arbitragem apresentam um mau desempenho. Os três modelos assumem que a matriz de variâncias e covariâncias dos fatores latentes em cada modelo é constante, o que limita a sua utilidade no cálculo do Valor em Risco. Portanto, os modelos que relaxam esta hipótese devem proporcionar melhor desempenho e ser mais adequados para a gestão de risco das carteiras de renda fixa.

Dormancy is an adaptive strategy in plants to survive under unfavorable climatic conditions during winter. In temperate regions, most fruit trees need exposure to a certain period of low temperatures to overcome endodormancy. After endodormancy release, exposure to warm temperatures is needed to flower (ecodormancy). Chilling and heat requirements are genetically determined and, therefore, are specific for each species and cultivar. The lack of sufficient winter chilling can cause failures in flowering and fruiting, thereby compromising yield. Thus, the knowledge of the chilling and heat requirements is essential to optimize cultivar selection for different edaphoclimatic conditions. However, the lack of phenological or biological markers linked to the dormant and forcing periods makes it difficult to establish the end of endodormancy. This has led to indirect estimates that are usually not valid in different agroclimatic conditions. The increasing number of milder winters caused by climatic change and the continuous release of new cultivars emphasize the necessity of a proper biological marker linked to the endo- to ecodormancy transition for an accurate estimation of the agroclimatic requirements (AR) of each cultivar. In this work, male meiosis is evaluated as a biomarker to determine endodormancy release and to estimate both chilling and heat requirements in apricot. For this purpose, pollen development was characterized histochemically in 20 cultivars over 8 years, and the developmental stages were related to dormancy. Results were compared to three approaches that indirectly estimate the breaking of dormancy: an experimental methodology by evaluating bud growth in shoots collected periodically throughout the winter months and transferred to forcing chambers over 3 years, and two statistical approaches that relate seasonal temperatures and blooming dates in a series of 11–20 years by correlation and partial least square regression. The results disclose that male meiosis is a possible biomarker to determine the end of endodormancy and estimate AR in apricot.

We study a dynamic model of labor migration in which moving costs decrease with the number of migrants already settled in the destination. This assumption is supported by sociological studies of migrant networks. With endogenous moving costs migration occurs gradually over time. Once it starts, it develops momentum, and migratory flows may increase even as wage differentials narrow. In addition, migration tends to follow geographical channels, and low-moving-cost individuals migrate first. These patterns are consistent with historical evidence from the Great Black Migration of 1915-1960, much of which cannot be reconciled with existing migration models.

In this paper, we illustrate in-depth Bayesian hierarchical statistical modeling ap-proaches. Bayesian hierarchical modeling provides a robust framework for analy-zing spatial data, accommodating complex dependencies, making possible incorporating external knowledge into the analysis. To do so, we consider a dataset from 80 stations in the Venezuelan state of Guárico consisting of accumulated monthly rainfall in a time span of 16 years. The spatial correlation is modeled by using a Matérn correlation function with a fixed smoothness parameter. Following Banerjee et al. (2014), we examine two fully Bayesian parametric approaches: One ofthem static, based on a hierarchical model with latent variables; and the other spatiotemporal, based on the dynamic framework given in West and Harrison (2006). Both alternatives are sensible ones, but due to the nature of the data, the dynamic model is more appealing since it gives a complete spatiotemporal characterization of the response variable. En este documento, ilustramos enfoques de modelado estadístico jerárquico Bayesiano en profundidad. El modelado jerárquico Bayesiano proporciona un marco sólido para analizar datos espaciales, acomodando dependencias complejas, haciendo posible la incorporación de conocimiento externo al análisis. Para hacerlo, consideramos un conjunto de datos de 80 estaciones en el estado venezolano de Guárico, asociado con la precipitación mensual acumulada en un periodo de 16 años. La correlación espacial se modela utilizando una función de correlación de Matérn con un parámetro de suavidad fijo. Siguiendo a Banerjee et al. (2014), examinamos dos enfoques paramétricos completamente Bayesianos: uno de ellos estático, basado en un modelo jerárquico con variables latentes; y el otro espaciotemporal, basado en el marco dinámico de West y Harrison (1997). Ambas alternativas son importantes, pero debido a la naturaleza de los datos, el modelo dinámico es más atractivo ya que proporciona una caracterización espaciotemporal completa de la variable respuesta.

Rakstā aplūkotas sistēmdinamiskās modelēšanas iespējas, kuras var pielietot Engures ezera sateces baseina vides procesu modelēšanā. Aplūkoti sistēmdinamikas modeļu veidošanas pamatprincipi un to svarīgākie elementi un uzbūve. Raksta mērķis ir iepazīstināt ar iespējamām ekosistēmu modelēšanas metodēm, kas ļauj vispusīgi atspoguļot ekosistēmas faktisko stāvokli un prognozēt tālāko procesu virzību ekosistēmās. Šis ekosistēmas modelēšanas paņēmiens dod iespēju atspoguļot antropogēnos un dabiskos procesus, kas ietekmē ekosistēmas. Sistēmdinamiskie modeļi tiek veidoti, lai parādītu dažādu procesu savstarpējo mijiedarbību, kā arī novērtētu svarīgākos vidi ietekmējošos faktorus. Šī modeļa darbības pamatā ir blokshēmu un algoritmu sistēmas, kas ar matemātisku funkciju palīdzību attēlo notiekošās izmaiņas grafisku sakarību vai tabulu veidā. Engures ezera sateces baseina gadījumā tiek meklētas kopsakarības starp faktoriem, kas veido dažādas ekosistēmas. Modeļu veidošanai izmantota specializēta datorprogramma PowerSim Studio Academic 9.0. Modelī ietilpst visaptveroši dati par dažādām ekosistēmām, piemēram, ūdens ķīmiski fizikālie, hidroloģiskie, bioloģiskie, ornitoloģiskie un citi parametri. Pēc šīs modelēšanas metodes izstrādes būs iespējams novērtēt dažādu procesu ietekmi uz bioloģisko daudzveidību pētījumu teritorijā, kā arī, konstatējot būtiskas problēmas, uzlabot vides pārvaldības praksi apkārtējās pašvaldībās un risināt svarīgas problēmas citos reģionos. This focus of the study was on system dynamic models that could be useful for modelling environmental processes in Lake Engure. The paper considers the system dynamic model development principles, the most important elements and structure. The aim of the study was to describe possible methods of ecosystem process modelling that allow to represent the actual state of ecosystems and provide opportunities to predict further processes. The methods of ecosystem modelling considered in the paper reveal interactive factors of anthropogenic and environmental processes that influence changes in ecosystems. System dynamic models indicate not only interactions between various factors in the environment but also the most important driving forces. These models are based on flowchart and algorithm systems, which represent changes using mathematical functions in a graphic or tabular form. In the case study of Lake Engure, connections between factors that influence ecosystems in the study area were identified. Specialised software, PowerSim Studio Academic 9.0, was used for modelling. The model consists of qualitative and multifactor data of Lake Engure ecosystems, such as water chemical, physical and hydrological parameters, biological, ornithological and other data collected in the study area. Development of this modelling method will make it possible to evaluate the impact of various processes on biological diversity changes in the study area and to identify the most important problems. Furthermore, this method could improve environmental management practice in the surrounding municipalities, and it will also be possible to make similar models of ecosystem quality in other regions.

Laboratory experiments were used to investigate the influences of 25 combinations of temperature and barley plant growth stage (5 x 5 factorial combination of temperature and barley plant growth stage) on the development, survival and reproduction of the Russian wheat aphid (RWA), Diuraphis noxia. For each of the 25 treatments, the developmental time and nymphal production of 72 RWA individuals were recorded (1,800 RWA in total) throughout their entire lifetimes. The collected data were used for analyzing demography, modelling phenology, and simulating population growth of RWA. In this paper, the results of demographic analyses are reported. Specifically, for each treatment, cohort life tables, reproductive heterogeneity tables (parity and birth intervals), and reproductive schedule tables were constructed, and demographic parameters such as intrinsic rate of increase, life-span, fecundity, life table entropy, etc. calculated. Based on these analyses, the most important summary demographic statistics are reported. Using the intrinsic rate of increase (rm) as an example, a procedure is demonstrated that builds a dynamic rm model by applying the Best Subset Regression approach. A more comprehensive (considering reproductive heterogeneity and schedule tables) yet concise (comprising dynamic rm models) demographic model than that based on standard life table analysis alone is presented.

The complexity of supply chains requires advanced methods to schedule companies' inventories. This paper presents a comparison of model forecasts of demand for multiple products, choosing the best among the following: autoregressive integrated moving average (ARIMA), exponential smoothing (ES), a Bayesian regression model (BRM), and a Bayesian dynamic linear model (BDLM). To this end, cases in which the time series is normally distributed are first simulated. Second, sales predictions for three products of a gas service station are estimated using the four models, revealing the BRM to be the best model. Subsequently, the multi-product inventory model is optimized. To define the policies for ordering, inventory, costs, and profits, a Bayesian search integrating elements of a Tabu search is used to improve the solution. This inventory model optimization process is then applied to the case of a gas service station in Colombia.

Dado el amplio uso de los datos de panel en modelos dinamicos, es relevante evaluar el desempeno de sus diferentes estimadores en muestras finitas en presencia de baja y alta persistencia. El presente articulo tiene como objetivo analizar, mediante simulaciones tipo Monte Carlo, las propiedades de los estimadores de efectos fijos (LSDV), Arellano y Bond (AB-GMM1), Blundell y Bond (BB-GMM1), Anderson y Hsiao (AH) y Kiviet. Se concluye que en series no persistentes el estimador de Kiviet es el de mejor desempeno, basandose en los criterios de error cuadratico medio, sesgo y desviacion estandar; con alta persistencia, el estimador BB-GMM1 es el de mejor desempeno seguido por el estimador de Kiviet, que se comporta bien excepto en micropaneles con series persistentes. Given the widespread use of panel data in dynamic models, it is worth evaluating the performance of different estimators in finite samples in the presence of low and high persistence, with the latter being present in many macroeconomic series. This article analyzes the properties of the Least Square Dummy Variable (LSDV) estimators, Arellano-Bond Generalized Method of Moments Stage 1 (AB-GMM1), BBGMM1 (), AH (Anderson-Hsiao), and Kiviet using a Monte Carlo experiment. The results show that, in the presence of low persistence, the Kiviet estimator is the best performer based on the criteria of Root-Mean-Square Error (RMSE), bias and standard deviation. Meanwhile in the case of high persistence, the system estimator of Blundell and Bond (GMM1) is the best performing estimator against their rivals, followed by Kiviet estimator that exhibits good behavior, except in micropanels. Devido a ampla utilizacao dos dados do painel em modelos dinamicos, e relevante avaliar o desempenho dos seus diferentes avaliadores em amostras finitas na presenca de baixa e alta persistencia. O presente artigo tem como objectivo analisar, atraves de simulacoes tipo Monte Carlo, as propriedades dos avaliadores de Efeitos Fixos (LSDV), Arrellano e Bond (AB-GMM1), Blundell e Bond (BB-GMM1), Anderson e Hsiao (AH) e Kiviet. Conclui-se que em series nao persistentes o avaliador de Kiviet e o de melhor desempenho baseando-se nos criterios de erro quadratico medio, obliquidade e desvio padrao; com alta persistencia o avaliador BB-GMM1 e o melhor desempenho seguido pelo avaliador de Kiviet que se comporta bem excepto em micro-paineis com series persistentes.