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We propose a nonlinear infinite moving average as an alternative to the standard state space policy function for solving nonlinear DSGE models. Perturbation of the nonlinear moving average policy function provides a direct mapping from a history of innovations to endogenous variables, decomposes the contributions from individual orders of uncertainty and nonlinearity, and enables familiar impulse response analysis in nonlinear settings. When the linear approximation is saddle stable and free of unit roots, higher order terms are likewise saddle stable and first order corrections for uncertainty are zero. We derive the third order approximation explicitly, examine the accuracy of the method using Euler equation tests, and compare with state space approximations.

Representation of continuous-time ARMA (Auto-Regressive-Moving-Average), CARMA, time-series models is reviewed. Computational aspects of simulating and calculating the likelihood-function of CARMA models are summarized. Some numerical properties are illustrated by simulations. Methods for enforcing the stationarity restriction on the parameter space are discussed. Due to such methods restricted numerical estimation enforcing stationarity is possible. The impact of scaling of time axis on the magnitude of the parameters is demonstrated. Proper scaling of the time axis can give parameter values of similar magnitude which is useful for numerical work. The practicality of the computational approach is illustrated with some real and simulated data.

Policy function iteration methods for solving and analyzing dynamic, stochastic general equilibrium models are powerful from both a theoretical and computational perspective. Despite obvious theoretical appeal, significant startup costs and a reliance on a grid-based method have limited the use of policy function iteration as a solution algorithm. We reduce these costs by providing a user-friendly suite of MATLAB functions that introduce multi-core processing and Fortran via MATLAB's executable function. We demonstrate why policy function iteration is particularly useful in solving models with regime-dependent parameters, recursive preferences, and binding constraints. We examine a canonical real business cycle model and a new Keynesian model that features regime switching in policy parameters, Epstein-Zin preferences, and monetary policy that occasionally hits the zero-lower bound to highlight the attractiveness of our methodology. We compare our advocated approach to other familiar computational methods, highlighting the tradeoffs between accuracy and speed.

This paper provides an efficient way to generate a set of random choices on a set of budgets which satisfy the Generalised Axiom of Revealed Preferences (GARP), that is, they are consistent with utility maximisation. The choices are drawn from an approximate uniform distribution on the admissible region on each budget which ensures consistency with GARP, based on a Markovian Monte Carlo algorithm due to Smith (1984). This procedure can be used to extend Bronars‘ (1987) method as it can be used to approximate the power of tests for conditions for which GARP is a necessary but not sufficient condition (e.g., homotheticity, separability, risk aversion, etc.). For example, it allows to approximate the probability that a set of random choices which happens to satisfy GARP is also consistent with homotheticity. The approach can also be applied to production analysis and nonparametric tests of cost minimisation.

Energy efficiency of buildings in the service sector is becoming increasingly important in China due to the structural shift of the economy from industry to services. This paper employs a bottom-up cohort model to simulate current energy saving policies and to make projections for future energy use and CO 2 emissions for the period 2000–2030 in the Chinese service sector. The analysis shows that energy demand in the service sector will approximately triple in 2030, far beyond the target of quadrupling GDP while only doubling energy use. However, it is feasible to achieve the target of emission reduction by 40% in 2020 even under the poor state of compliance rate of building standard. This paper also highlights four crucial aspects of designing optimal energy saving policies for China's service sector based on the model results.

This paper analyzes the dynamics of a variant of Jones (2002) semi-endogenous growth model within the feasible parameter space. We derive the long-run growth rate of the economy and do a detailed bifurcation analysis of the equilibrium. We show the existence of codimension-1 bifurcations (Hopf, Branch Point, Limit Point of Cycles, and Period Doubling) and codimension-2 (Bogdanov–Takens and Generalized Hopf) bifurcations within the feasible parameter range of the model. It is important to recognize that bifurcation boundaries do not necessarily separate stable from unstable solution domains. Bifurcation boundaries can separate one kind of unstable dynamics domain from another kind of unstable dynamics domain, or one kind of stable dynamics domain from another kind (called soft bifurcation), such as bifurcation from monotonic stability to damped periodic stability or from damped periodic to damped multiperiodic stability. There are not only an infinite number of kinds of unstable dynamics, some very close to stability in appearance, but also an infinite number of kinds of stable dynamics. Hence subjective prior views on whether the economy is or is not stable provide little guidance without mathematical analysis of model dynamics. When a bifurcation boundary crosses the parameter estimatesʼ confidence region, robustness of dynamical inferences from policy simulations are compromised, when conducted, in the usual manner, only at the parametersʼ point estimates.

As an attempt to fi ght global warming, many countries try to reduce CO2 emissions in the power sector by significantly increasing the proportion of renewable energies (RES-E). A highly intermeshed electricity transmission grid allows the achievement of this target cost-efficiently by enabling the usage of most favorable RES-E sites and by facilitating the integration of fluctuating RES-E infeed and regional electricity demands. However, construction of new lines is often proceeding very slowly in areas with a high population density. In this paper, we try to quantify the bene ts of optimal transmission grid extensions for Europe until 2050 compared to moderate extensions when ambitious RES-E and CO2 reduction targets are achieved. We iterate a large-scale dynamic investment and dispatch optimization model for Europe with a load-flow based transmission grid model, in order to determine the optimal deployment of electricity generation technologies and transmission grid extensions from a system integrated point of view. Main findings of our analysis include that large transmission grid extensions are needed to achieve the European targets cost-efficiently. When the electricity network is cost-optimally extended, 228,000 km are built until 2050, representing an increase of 76% compared to today. Further findings include substantial increases of average system costs for electricity until 2050, even if RES-E are deployed efficiently throughout Europe, the grid is extended optimally, and if signi cant cost reductions of RES-E are assumed.

We put forward a framework for measuring systemic risk and attributing it to individual banks. Systemic risk is measured as the expected loss to depositors and investors when a low-probability systemic event occurs. The risk contributions are calculated based on derivatives of the systemic risk measure and, thus, ensure a full risk allocation among institutions. We apply our approach to a panel of 54 to 86 of the world's major commercial banks, using 13 years of monthly data. This empirical exercise shows that, whereas the median systemic risk in the sample is about $3tr, it peaks at $20tr at the beginning of 2009. Thereby some 4 to 10 of the biggest contributors account for more than 50% of the system-wide risk, contributing considerably more than their relative size suggests. Based on data for banks' liabilities, default probabilities and asset correlations, we can match very closely the list of G-SIBs revealed by FSB. The individual risk contributions may not only be used for identification of systemically important banks. Being an estimate of negative externalities, they can also be used to compute bank-specific capital surcharges, as we describe in the paper. In addition to this cross-sectional dimension, we also address the time dimension of systemic risk and suggest a method for smoothing the cyclicality of the underlying risk measure. The analysis of risk drivers confirms that the main focus of macroprudential banking supervision should be on a solid capital base throughout the cycle and de-correlation of banks' asset values.

In this paper we focus on the specification of revenue functions in their dual price space. We consider two distance functions–the Shephard output distance function and the directional output distance function–and define both in price space. The former is multiplicative in nature and satisfies homogeneity, whereas the latter is additive and satisfies the translation property. Functional equation methods yield the translog specification in the case of the Shephard distance function and a quadratic specification in the case of the directional distance function. Monte Carlo evidence suggests that the quadratic specification outperforms the translog in large samples and in true models with plenty of curvature.

Türkiye Enerji Piyasası Düzenleme Kurulu motorin türlerine ilişkin yaptığı bir düzenleme ile piyasaya akaryakıt olarak arz edilen motorinin yerli tarım ürünlerinden üretilmiş dizel içeriğini 2014 yılı için en az %1, 2015 yılı için en az %2 ve 2016 yılı için ise en az %3 olarak belirlemiştir. Bu çalışma, belirlenen hedeflerin tarım sektörü ve kırsal kesim gelirleri açısından etkilerini analiz etmektedir. Çalışmada çok-ülkeli, çok-ürünlü, ikili ticarete izin veren bir tarım ticaret modeli ve 2010 yılı için oluşturulmuş sosyal hesaplar matrisinden türetilen fiyat çarpanları kullanılmaktadır. Koşulan senaryolar biyo-dizel talebinin büyük ölçüde ayçiçeği ve daha küçük çapta soya arzının arttırılmasıyla karşılanması gerektiği yönünde bulgular ortaya koymaktadır. Bu arz artışının ise eş zamanlı olarak hem yurtiçinde prim verilerek hem de bu ürünlerin ham ve yağlarının ithalatında tarife indirimi yapılarak gerçekleştirildiği takdirde göreli olarak daha düşük maliyetle sağlanacağı gözlenmektedir. Kırsal kesimde faktör ve hanehalkı gelirleri ise prim artış ve tarife indirim oranlarına göre düzenlenebilecektir. Fakat sonuç itibarı ile Türkiye’nin fosil yakıt kullanımında biyo-dizel yerine biyo-etanol ikamesine yönelmesi daha rasyonel bir seçim olacaktır.

As an important project type of Agile Software Development, the performance evaluation and prediction for eXtreme Programming project has significant meanings. Targeting on the short release life cycle and concurrent multitask features, a statistical earned value analysis model is proposed. Based on the traditional concept of earned value analysis, the statistical earned value analysis model introduced Elastic Net regression function and Laplacian hierarchical model to construct a Bayesian Elastic Net model fitted for project performance evaluation and prediction. The model is demonstrated with the JAX Laboratory software development project data. With simulated coefficients estimation, we realized an empirical data support for project performance assessment.

Forecasting Populations (FPOP) is a microsimulation model (MSM) that is the demographic core of an extensible modeling framework. The framework, with FPOP at its core, enables the geospatial projection of a population under purely demographic processes or under the additional influence of exogenous factors such as disease, policy changes and prevention programs, or environmental stressors. Empirically-derived transition probabilities of life events such as birth, death, marriage, divorce and migration, captured in lookup table format, drive the simulation. These transition probabilities can be modified dynamically by external user-defined functions or other external MSMs. The use of MSM structures and methodologies enables FPOP to portray the impact of heterogeneity in the geospatial dimension (e.g., distribution of environmental factors or distribution of intervention programs), as well as the social dimension (e.g., household or social network correlates), on the projections. POP is designed and structured to: enable linking with external MSMs of any kind; support inclusion or configuration of more detailed transition probabilities; be scalable to millions of agents; use either an existing baseline synthetic population or a custom synthetic population of the user’s design; and, run under computing environments that don’t require a high degree of specialized software or hardware. In this paper we describe the design and structure of FPOP and then apply FPOP first under purely demographic processes and, secondly, in conjunction with an external disease model of obesity.The objective of FPOP is to provide a demographically realistic projection of the size, structure, and movement of populations and households decades into the future.

Detection turning points in unimodel has various applications to time series which have cyclic periods. Related techniques are widely explored in the field of statistical surveillance, that is, on-line turning point detection procedures. This paper will first present a power controlled turning point detection method based on the theory of the likelihood ratio test in statistical surveillance. Next we show how outliers will influence the performance of this methodology. Due to the sensitivity of the surveillance system to outliers, we finally present a wavelet multiresolution (MRA) based outlier elimination approach, which can be combined with the on-line turning point detection process and will then alleviate the false alarm problem introduced by the outliers.

Based on the report for the unit “Foresight Methods Analysis” of the PhD programme on Technology Assessment at the Universidade Nova de Lisboa, under the supervision of Prof. Dr. António B. Moniz Healthcare systems are characterized by a rapid technology push and demand. Most decisions taken in this field usually are accompanied by risk and uncertainties. Due to financial constrains (common in every healthcare system), all decision must be made based on evidences of reliable studies. It may be not possible to know the future needs of healthcare systems in general, but it should be possible to foreseen them. This paper aims to shed some light on how prospective analysis can be an adding tool for the decision-making process, by enhancing exploratory and strategic dimension of planning and managing in a sensitive field such as healthcare. In order to provide for a simple overview on foresight exercises applied to health, this paper presents two study-cases that applied different methods. Since different foresight methods were applied, a comparative case-study analysis was applied, taking into consideration the following aspects of the exercise: aim, methodology, stakeholders and outputs. The specific objectives of this report are: to explore the usage of foresight methods applied to healthcare level in two different countries and therefore to understand if there are any similarities in the approach; and based on the analysis results, to develop recommendations for healthcare level decision-making in general. The results of this report can be useful for a better understanding on how foresight methods can be applied in healthcare and their importance. This article can help healthcare professionals, providing them a glimpse of some steps on the use of these foresight methods, so they can be more alert for foresight methodological framework and their practical applications. The knowledge on how to apply foresight methodology can be a differential and potential asset of a well-organized and informed institution, as well as an asset for a shared and participative strategic planning. António B. Moniz

This article investigates the Multiple Equilibria Regulation (MER) model, i.e., an agent-based simulation model, to represent opinion dynamics in social networks. It relies on a small set of micro-prerequisites (intra-individual balance and confidence bound), leading to emergence of (non)stationary macro-outcomes. These outcomes may refer to consensus, polarization or fragmentation of opinions about taxation (e.g., congestion pricing) or other policy measures, according to the way communication is structured. In contrast with other models of opinion dynamics, it allows for the impact of both the regulation of intra-personal discrepancy and the interpersonal variability of opinions on social learning and network dynamics. Several simulation experiments are presented to demonstrate, through the MER model, the role of different network structures (complete, star, cellular automata, small-world and random graphs) on opinion formation dynamics and the overall evolution of the system. The findings can help to identify specific topological characteristics, such as density, number of neighbourhoods and critical nodes-agents, that affect the stability and system dynamics. This knowledge can be used to better organize the information diffusion and learning in the community, enhance the predictability of outcomes and manage possible conflicts. It is shown that a small-world organization, which depicts more realistic aspects of real-life and virtual social systems, provides increased predictability and stability towards a less fragmented and more manageable grouping of opinions, compared to random networks. Such macro-level organizations may be enhanced with use of web-based technologies to increase the density of communication and public acceptability of policy measures.

This paper presents the numerical simulation of the customer requests processing by generalists and specialists in a financial institution using ARENA software. The model considers three types of requests: standard requests, direct special requests and special requests received by telephone or e-mail. The requests processing time and costs receive a detailed analysis: the processing time, the waiting time and the total time, the requests number and the requests cost dependencies as a function of the standard requests incoming frequency are presented.

La presente investigación estudia el impacto económico un conjunto de delitos tipificados en Ecuador como: robo total de vehículo, robo accesorios de vehículos, robo de vivienda, robo a personas, estafa, intimidación o amenaza y heridas. Para cuantificar el costo económica que representa para la sociedad la actividad delictiva desarrollamos un modelo que estima las pérdidas esperadas y las inesperadas, utilizando la metodología Loss Distrbution Aprroach (LDA). En este trabajo, empleamos los datos de la Encuesta de Victimización y Percepción de Inseguridad 2011, realizada por el Instituto Nacional de Estadísticas y Censos (INEC), y los registros administrativos provenientes de la Dirección General de Operaciones de la Policía Nacional.

In the present paper we will try to analyze the Decision Support System (DSS) and the way in which it is applied or not in the Romanian Small and Medium Sized Enterprises (SMEs) (with examples). We also will see if the system is beneficial for these Romanian Enterprises. We analyzed through interviews 50 small and medium-sized Romanian enterprises. They do not accept their name to be published. As a consequence, we will present only the results. It is underlined in the conclusions the differences between the small and the medium size enterprises in respect of the models they are using. The most important benefits of DSS (generally) are represented by increased efficiency, competitive advantages and better managerial process.

The extension of the European Union with the last two “waves†of new members in 2004 and later in 2007 brought new opportunities for the countries in Eastern Europe, especially those linked with the usage of European funding to support public or private investments. It is obvious that “effective utilisation of EU support can foster the success of economic performance†(European Commission, 2009). Financing investment projects proposed by the SMEs can be realized through several financial programmes, especially those established under the European Regional Development Fund (ERDF). In order to be approved investment projects must comply with the requirements of the financing program especially those related to a proper realized Cost Benefit Analysis (CBA). A major emphasis within the CBA consists from sensitivity and risk assessment modelling. Sensitivity analysis aims to identify those variables / risks that may impact during project development and operational period and may lead to failure of positive factors leading to significant change in the financial and economic profitability of the project. Sensitivity analysis used to measure consider identifying risk factors that have the greatest influence on the net present value in general, and in particular on EU-funded projects, the financial and economic rates from financial modelling to analyse cost-benefit analysis (CBA) (including rate gap financing for investment projects where required) and indicate their impact during project cycle reg. Sensitivity analysis can help identify poor design choices and can highlight the need to obtain further information on certain variables (European Commission, 2008). Regarding this, the current paper has as its main objectives the analysis of the decision system for approving an EU funded project. The secondary objective is to analyze the proposed indicators within the sensitivity and risk assessment system which are used for selection of investment projects. The analysis is made using as a case study an investment project developed by Romanian companies. Both approaches of sensitivity analysis: deterministic and stochastic analysis, were tackled within the paper. As final conclusions of this paper we will demonstrate the theoretical and practical role of cost-benefit analysis â€" sensitivity and risk assessment to select the best applications that will be proposed for funding under the European grant programs.

This paper describes characteristics of a discrete - time model for a consumers group through bounded rationality theory, where the basis of decision-making is the Aspiration Adaptation Theory (Selten, 1998). Each consumer try to imitate the decision of others related to them based in last time period observation, as long as they are connected by no directed and no weight graph. The models consist of some algorithms for imitation process, income raise through education and the extreme cases. Finally, the paper shows conclusions and extensions.

This paper applies non-linear methods to analyze and predict the daily open S&P index which is one of the most important stock index in the world. The aim of the analysis is to quantitatively show if the corresponding time series is a deterministic chaotic one and if one or more days ahead prediction can be achieved. These results make the present work a valuable tool for traders investors and funds.

We present ongoing work on the construction of a spatial microsimulation model to assess the influence of demographics on residential heat consumption for Hamburg, Germany. Demographics are important for urban energy planning as: (1) Buildings are becoming more energy-efficient and building occupant behaviour accounts for a growing share in the variation of consumption; (2) building occupant needs are changing along with demographic change; and (3) the share of small decentralized district heating grids, in which fewer customers mean less averaging out of heterogeneous occupant profiles, is set to play a bigger role in the country’s heat supply. We construct a spatial microdata set for the city of Hamburg (of roughly 1.8 million inhabitants and 370 000 buildings), with households populating geo-referenced buildings, in three steps: (a) Synthesizing the population of small scale “statistical areas”, comprising up to around 2000 people (we do this by selecting households recorded in the German microcensus and fitting them into the statistical areas); (b) assigning energy relevant properties to the geo-reference buildings from the Hamburg digital cadaster (we do this by making use of a well-established building typology developed for energy assessment) and constructing dwelling units in these buildings; and (c) matching households to the dwelling units in these buildings (which we do again by using household data from the microcensus). This last step–allocating households to buildings–may be the most interesting and challenging task. As of to date, we use a combinatorial optimization algorithm to achieve this. Once we have a microsimulation model of buildings and households living in them, including their demographic composition, the range of questions that can be explored is immense. The illustration presented here is a simple heat balance computation of individual buildings, using the constructed socio-demographic data and the digital cadaster data as input parameters.

Standard solution methods for linear stochastic models with rational expectations presuppose a time-invariant structure. Consequently, credible announcements that entail future changes of the structure cannot be handled by standard solution methods. This paper develops the solution for linear stochastic rational expectations models in the face of a finite sequence of anticipated structural changes. These events encompass anticipated changes to the structural parameters and also anticipated additive shocks. We apply the solution to some examples of practical relevance to monetary policy. © 2013 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

In this study we evaluate the distortion of the ratio of non-performing loans (NPL) caused by rapid credit growth to show that the bias in this ratio (caused by the prolonged credit boom) may indeed be significant. Next, we discuss an adjustment to the NPL ratio based on a theoretical model of a loan portfolio. This adjustment is robust for credit booms and busts; therefore, it can be used to compare credit quality ratios across distinct portfolios and banks as well as to simulate future NPL ratio developments. Our estimates of the portfolio of housing loans in Poland show that the new adjusted index of non-performing loans is robust to different model specifications.

The present paper questions the financial efficiency of the most used market portfolio proxies in Spain and Mexico (IBEX35 and IPC) in order to determine if these can be considered a proper market portfolio proxy. The paper questions if they can be used as “neutrals”, according to Black & Litterman (1992) proposals in portfolio management. For this purpose, two discrete event simulations that use the Markowtiz-Tobin-Sharpe-Linter model (Markowitz, 1987, p.5) are performed with monthly data of the stock members of these indices in a February 2001 to December 2010 time window. The results are compared by using the Sharpe ratio (Sharpe, 1966) and show that the equilibrium assumptions in the market do not hold, leading to conclude that these market portfolio proxies are inefficient.

With assets taken to be pools, rational expectations on their delivery rates are, as default is permissible in an economy and its penalty prescribed in terms of utility, indispensable to the existence of equilibrium. And the resulting equilibrium relies heavily on the prevailing penalty level. We propose, in this paper, a path-following algorithm for calculating the level of penalty that leads to a Pareto efficient equilibrium—Pareto efficient among the set of equilibria of the economies with distinct penalty levels. This algorithm brings off those rational expectations by identifying their upper and lower bounds iteratively until the discrepancies between them are admissible.

Developing new methods for predictive modeling of time series and application of the existing techniques in other areas will be a permanent concern for both researchers and companies that are interested in gaining competitive advantages. In this paper I present the construction of an artificial intelligence model, based on Support Vector Machines that predict the exchange rate DOLLAR/EURO. For simulations I ve used Matlab software suite.

In the market economy, when the relationship of theproduct-market - competition becomes more and more complex,the task of the deciders is increasingly difficult. A modern organization must have a management style based on the flexibility, dynamism and foresight. In conditions of diversifying of the current economic-social environment, adopting the decision involves not only the call to inspiration, talent or luck, but also to use the scientific methods to approach and resolve the leading process dilemmas of the economic systems. The problem of the decision-making is the activities difficulty of the collecting, processing and selection of the organization information, in general. The relevant integrated information into the decision-making is an important resource for an organization, when there is quality and obtained and used it in a right time. The decisions-making, therefore, is based on the complex, operational and quality information.

Für den Ausgleich von Schwankungen der Stromnachfrage und des Stromangebotes aus erneuerbaren Energien sind Kraftwerke nötig, die auch in kritischen Situationen sicher zur Verfügung stehen. Es wird diskutiert, ob der liberalisierte Strommarkt in der Lage ist, ausreichende Investitionen anzuregen. Vielfach werden unterstützende Instrumente gefordert. Diese auch als Kapazitätsmechanismen bezeichneten Instrumente haben je nach Typ unterschiedliche Wirkung auf Stromerzeuger und -verbraucher. Unsere Modellrechnungen zeigen, dass die Gesamtbelastungen zur Erreichung einer Reservemarge von fünf Prozent im Jahr 2020 in Deutschland zwischen 0,4 und 3,1 Milliarden Euro betragen und in Summe zu deutlichen Mitnahmeeffekten bei den etablierten Stromerzeugern führen. Dem gegenüber stehen die in dieser Untersuchung nicht quantifizierten Vorteile einer Reduktion des Risikos von Stromausfällen. -- -- In order to compensate for fluctuations in electricity demand as well as electricity supply from renewables, power plants are required that are reliably available, even in critical situations. Besides an ongoing discussion whether the liberalized electricity market is capable of stimulating sufficient investment, there are frequent calls for supporting instruments. Depending on their type, these instruments, also known as capacity mechanisms, have different effects on electricity producers and consumers. Model calculations prepared by DIW Berlin demonstrate that the total burden for achieving a defined safety margin in 2020 in Germany is between 0.4 and 3.1 billion euros and that overall, it leads to considerable free-rider effects on the part of the established electricity producers. This burden is potentially offset by advantages from the reduction of the risk of blackouts, which are difficult to quantify.

Households “sort” across neighborhoods according to their wealth and their preferences for public goods, social characteristics, and commuting opportunities. The aggregation of these individual choices in markets and in other institutions influences the supply of amenities and local public goods. Pollution, congestion, and the quality of public education are examples. Over the past decade, advances in economic models of this sorting process have led to a new framework that promises to alter the ways we conceptualize the policy evaluation process in the future. These “equilibrium sorting” models use the properties of market equilibria, together with information on household behavior, to infer structural parameters that characterize preference heterogeneity. The results can be used to develop theoretically consistent predictions for the welfare implications of future policy changes. Analysis is not confined to marginal effects or a partial equilibrium setting. Nor is it limited to prices and quantities. Sorting models can integrate descriptions of how nonmarket goods are generated, estimate how they affect decision making, and, in turn, predict how they will be affected by future policies targeting prices or quantities. Conversely, sorting models can predict how equilibrium prices and quantities will be affected by policies that target product quality, information, or amenities generated by the sorting process. These capabilities are just beginning to be understood and used in applied research. This survey article aims to synthesize the state of knowledge on equilibrium sorting, the new possibilities for policy analysis, and the conceptual and empirical challenges that define the frontiers of the literature. (JEL C63, D04, E61, H41, R23, R31, R38)

A common difficulty faced when projecting small area populations has to do with the implementation of local factors which highly influence local population growth. This problem is particularly important when simulating events that have a strong geographic component, such as internal migration, and destination choice of external migrants. This paper is part of a larger research program that aims at developing a dynamic time-based microsimulation projection model of the population of the Montreal metropolitan area and its 79 municipalities, by age, sex, and language. It presents an innovative treatment of migration by taking into consideration previous changes in local conditions when determining mobility and destination choices. Municipalities are first divided into two categories, suburban and the central city, to compute location-specific probabilities of moving by age, anguage and place of birth. Then, the destination choice of movers is based on a utility function that accounts for five important determinants of residential choice (1) the distance from the central city, (2) the current size of the population of each municipality, (3) the municipality development potential, (4) the linguistic composition of its population, and (5) the geographical location. A similar function is estimated to allocate a place of residence to external migrants. Parameters are estimated using conditional logistic regressions and are implemented into the microsimulation model. The model is validated over the 2006-2011 period and results of the reference scenario presented for 2031.

The complexity of credit-money is conceived as the central issue in the banking-macro nexus, which the author considers as a structural as well as process component of the evolving economy. This nexus is significant for the stability as well as the fragility of the economic system, because it connects the monetary with the real domain of economic production and consumption. The evolution of credit rules shapes economic networks between households, firms, banks, governments and central banks in space and time. The properties and characteristics of this evolutionary process are discussed in three sections. First, the author looks into the origins of the theory of money and its role for contemporary monetary economics. Second, he briefly discusses current theoretical foundations of top-down as well as bottom-up approaches to the banking-macro nexus, such as dynamic stochastic general equilibrium and agent-based models. In the third part he suggests an evolutionary framework, building on a generic rule-based approach, to arrive at standards for bottom-up foundations in agent-based macroeconomic models with a banking sector.

O artigo investiga se o tamanho das redes sociais pode afetar a taxa de desemprego da economia. Para tanto, com base em um modelo teórico de matching, propõe-se uma técnica de simulação dos efeitos das redes na empregabilidade dos trabalhadores. A proposta é aplicada em uma base de dados de Portugal, observando-se evidências que de que existe um tamanho ótimo de rede que minimiza a taxa de desemprego.

Mediante simulación, se estudian varios casos de fraude provocados por pirámides financieras tipo Ponzi (incluyendo los casos Madoff, DRFE y Cabrera); la técnica empleada corresponde a la implementación computacional de un modelo estocástico diseñado por J. Mayorga-Zambrano. Se comparan datos reales con aquellos generados por el software implementado; en particular, se estudia la evolución del número de clientes, del monto de estafa y del tiempo estimado de duración de la pirámide.

The Cross Entropy method is a well-known adaptive importance sampling method for rare-event probability estimation, which requires estimating an optimal importance sampling density within a parametric class. In this article we estimate an optimal importance sampling density within a wider semiparametric class of distributions. We show that this semiparametric version of the Cross Entropy method frequently yields efficient estimators. We illustrate the excellent practical performance of the method with numerical experiments and show that for the problems we consider it typically outperforms alternative schemes by orders of magnitude.

International audience; Given a settled reduction in the present level of tax revenue, and by exploring a very large combinatorial space of tax structures, in this paper we employ a genetic algorithm in order to determine the 'best' structure of a real world personal income tax that allows for the maximisation of the redistributive effect of the tax, while preventing all taxpayers being worse off than with the present tax structure. We take Italy as a case study.

This paper investigates the success of the well-known reverse-shooting and forward-shooting algorithms in finding stable solutions for linear macroeconomic models that both possess the particular property known as saddle-path instability and also have highly cyclic dynamic properties. It is anticipated that assessing how well these algorithms cope with solving highly cyclic models will also provide insights into how well they are likely to cope with solving non-linear models. In this paper, a perfect foresight version of the well-known Cagan (1956) model of the monetary dynamics of hyperinflation is augmented with a labour market. Additional eigenvalues are then generated through sluggish adjustment mechanisms for wages and for the supply of labour. This process provides the simplest model with stable complex-valued eigenvalues and a saddlepath: a model with two stable complex-valued eigenvalues and one unstable real-valued eigenvalue. Using this model, it is possible to define an indexing parameter that, when varied, determines a range of values for the stable eigenvalues: from (i) real-valued, to (ii) complex-valued with small absolute imaginary part, to (iii) complex-valued with large absolute imaginary part. This leads to corresponding stable time-paths for the model, which are (i) either humped or monotonic, (ii) cyclic but with infrequent cycles, and (iii) cyclic but with frequent cycles. This paper then compares the properties of solutions derived using the reverseshooting and forward-shooting approaches as the magnitude of the indexing parameter (and hence of the cycles) is allowed to vary. In the highly oscillatory case, we show that the success of both approaches is crucially dependent on the choice of ODE solver and of parameters.

© 2016 Informa UK Limited, trading as Taylor & Francis Group. In this paper, we develop a methodology for simultaneous recovery of the real-world probability density and liquidity premia from observed S&P 500 index option prices. Assuming the existence of a numéraire portfolio for the US equity market, fair prices of derivatives under the benchmark approach can be obtained directly under the real-world measure. Under this modelling framework, there exists a direct link between observed call option prices on the index and the real-world density for the underlying index. We use a novel method for the estimation of option-implied volatility surfaces of high quality, which enables the subsequent analysis. We show that the real-world density that we recover is consistent with the observed realized dynamics of the underlying index. This admits the identification of liquidity premia embedded in option price data. We identify and estimate two separate liquidity premia embedded in S&P 500 index options that are consistent with previous findings in the literature.

This paper is meant to analyse the e ects of labour market structural reforms by means of an agent-based model. Building on Dosi et al. (2016b) we introduce a policy regime change characterized by a set of structural reforms on the labour market, keeping constant the structure of the capital- and consumption-good markets. Con rming a recent IMF report (Jaumotte and Buitron, 2015), the model shows how labour market structural reforms re- ducing workers' bargaining power and compressing wages tend to increase (i) unemployment, (ii) functional income inequality, and (iii) personal income inequality. We further undertake a global sensitivity analysis on key variables and parameters which con rms the robustness of our findings.

We provide a survey of the micro and macro economics of climate change from a complexity science perspective and we discuss the challenges ahead for this line of research. We identify four areas of the literature where complex system models have already produced valuable insights: (i) coalition formation and climate negotiations, (ii) macroeconomic impacts of climate-related events, (iii) energy markets and (iv) diffusion of climate-friendly technologies. On each of these issues, accounting for heterogeneity, interactions and disequilibrium dynamics provides a complementary and novel perspective to the one of standard equilibrium models. Furthermore, it highlights the potential economic benefits of mitigation and adaptation policies and the risk of under-estimating systemic climate change-related risks.

The optimal management of a non-renewable resource extraction project is studied when input and output prices follow correlated stochastic processes. The decision problem is specified by two Bellman equations describing the project when it is currently operating or mothballed. Solutions are determined numerically using the Least Squares Monte Carlo methodology. The analysis is applied to an oil sands project which uses natural gas during extracting and upgrading. The paper takes into account the co-movement between crude oil and natural gas prices and proposes two price models: one incorporates a long-run link between the two while the other has no such link. Incorporating a long-run relationship between oil and natural gas prices has a significant effect on the value of the project and its optimal operation and reduces the sensitivity of the project to the natural gas price process.

We use a Monte Carlo experiment to compare the quadratic and translog functional forms in terms of their ability to approximate known frontiers that possess convex curvature. Unlike some of the existing simulation studies that have considered concave frontiers, we find that both functional forms provide a reliable approximation when a true frontier is convex. Our results lend support to existing intuitive explanations concerning the translog form’s innate propensity to yield convex, rather than concave, frontier estimates, suggesting that it should fare relatively well when modeling input isoquants. We also demonstrate that the quadratic functional form loses less of its flexibility than the translog function when shape constraints are imposed to satisfy regularity.

Wages are an element of cost crucially affecting the competitiveness of individual rms. But the wage bill is also a crucial element of aggregate demand. Hence it could be that more “flexible"and fluid labour markets, while allowing for faster inter-firm reallocation of labour, may also render the whole economic system more fragile, more prone to recession, more volatile. In this work we investigate some conditions under which such a conjecture applies. The paper presents an agent-based model that investigates the effects of two “archetypes of capitalism", in terms of regimes of labour governance - defined by the mechanisms of wage determination, firing, labour protection and productivity gains sharing - upon (i) labour market regularities and (ii) macroeconomic dynamics (long-term rates of growth, GDP fluctuations, unemployment rates, inequality, etc..). The model is built upon the \Keynes meets Schumpeter" family of models (Dosi et al.,2010), explicitly incorporating different microfounded labour market regimes. Our results show that seemingly more rigid labour markets and labour relations are conducive to coordination successes with higher and smoother growth.

Purpose The purpose of this paper is to provide some new empirical evidence on the weekend effect (one of the best known anomalies in financial markets) in Ukrainian futures prices. The analysis uses various statistical techniques. Design/methodology/approach The analysis uses various statistical techniques (average analysis, Student’s t-test, dummy variables, and fractional integration) to test for the presence of this anomaly, and then a trading simulation approach to establish whether it can be exploited to make extra profits. Findings The statistical evidence points to abnormal positive returns on Fridays, and a trading strategy based on this anomaly is shown to generate annual profits of up to 25 per cent. The implication is that the Ukrainian stock market is inefficient. Originality/value This paper provides some new empirical evidence on the weekend effect (one of the best known anomalies in financial markets) in Ukrainian futures prices. The analysis uses various statistical techniques (average analysis, Student’s t-test, dummy variables, and fractional integration) to test for the presence of this anomaly, and then a trading simulation approach to establish whether it can be exploited to make extra profits. The statistical evidence points to abnormal positive returns on Fridays, and a trading strategy based on this anomaly is shown to generate annual profits of up to 25 per cent. The implication is that the Ukrainian stock market is inefficient.

This paper develops an agent-based computational model of violent political revolutions in which a subjugated population of citizens and an armed revolutionary organisation attempt to overthrow a central authority and its loyal forces. The model replicates several patterns of rebellion consistent with major historical revolutions, and provides an explanation for the multiplicity of outcomes that can arise from an uprising. The relevance of the heterogeneity of scenarios predicted by the model can be understood by considering the recent experience of the Arab Spring involving several rebellions that arose in an apparently similar way, but resulted in completely different political outcomes: the successful revolution in Tunisia, the failed protests in Saudi Arabia and Bahrain, and civil war in Syria and Libya.

International audience; This paper extends the existing literature on strategic R&D alliances by presenting a model of innovation networks with endogenous absorptive capacity. The networks emerge as a result of bilateral cooperation over time between firms occupying different locations in the knowledge space. Social capital is ignored, and firms ally purely on the basis of knowledge considerations. Partner selection is driven largely by absorptive capacity which is itself influenced by cognitive distance and investment allocation between inventive and absorptive R&D. Cognitive distance between firms changes as a function of the intensity of cooperation and innovation. Within different knowledge regimes, we examine the structure of networks that emerge and how firms perform within such networks. Our model replicates some stylised empirical results on network structure and the contingent effects of network position on innovative performance. We find networks that exhibit small world properties which are generally robust to changes in the knowledge regime. Second, subject to the extent of knowledge spillovers, certain network strategies such as occupying brokerage positions or maximising accessibility to potential partners pay off. Third and most importantly, absorptive capacity plays an important role in network evolution: firms with different network strategies indeed differ in the build-up of absorptive capacity.

In this paper we build an agent-based model based on a threefold financial accelerator: (i) leverage accelerator - negative shocks on firms' output make banks less willing to loan funds, and firms less willing to make investments, hence a credit reduction follows further reducing the output; (ii) stock market accelerator - due to lower profit, firms' capitalization on the stock market decreases, thus the distance-to-default (DD) diminishes and it reinforces the leverage accelerator; (iii) network-based accelerator - the network structure may propagate the initial shock possibly resulting in an avalanche of bankruptcies. In this framework, we find that stock market volatility may damage the real economy if the stock market is too relevant. In particular, an increase of volatility worsens the economic performance through the stock market accelerator effect. Moreover, our findings have relevant implications for monetary policy.

Boom and bust cycles are widely documented in the literature on industry dynamics. Rigidities and delays in capacity adjustment in combination with bounded rational behavior have been identified as central driving forces. We construct a model that features only these two elements and we show that this is indeed sufficient to reproduce some stylized facts of a boom and bust cycle. The bifurcation diagrams summarizing the dynamic behavior reveal complex cycles and in particular also abrupt changes in the nature of these cycles. We apply new insights from the mathematical theory of piecewise smooth dynamic systems - in particular, results from the theory of border collision bifurcations - and show that the very existence of borders such as capacity constraints or nonnegativity constraints may lie behind abrupt changes in the dynamic behavior of economic variables.

This paper will be later used within the Doctoral thesis: “The Mechanism of Financing Investment Projects by Usage of European Structural Fundsâ€, which is currently under development at the University Babeș Bolyai Cluj Napoca, Faculty of Economics and Business Management, under the coordination of the prof. univ. dr. Ioan Trenca. This paper comes also as a result of the European Funded project PERINPRO “Cross-Border Research Programme - Performance Indicators of the Economic Entities from Bihor-Hajdu Bihar Euroregionâ€. The goal of the project was to identify of a set of common indicators that characterizes companies in the Bihor-Hajdu Bihar Euroregion and which will be used to analyze the financial health of the economic entities in the Euroregion of Hajdu-Bihar- Bihor. The first chapter of the paper will introduce the research and also will present the literature review and the methodological framework: by establishing a common set of indicators for the financial analysis of the companies located in the Bihor-Hajdu Bihar Euroregion. Seven of these indicators considered to be highly important will also briefly described and defined. Some of these indicators are used for the first time in a trans-national analysis over companies located in the Romanian-Hungarian cross border area. In the second chapter the research will be focused over establishing a common ground for usage of the financial reporting documents as basis for the analysis. Several characteristics which differentiate the financial reporting documents from Romania and Hungary will be identified and measures for correction of the values of the indicators will be proposed. This comparative study can be considered an innovation, as well, in the cross-border area since in the past no other studies of this types were performed between Romania and Hungary. The third chapter will be focused over the application of seven identified common indicators to companies based in the cross-border area. In the last part of the paper these results will be analyzed and presented.

The extension of the European Union with the first “wave†of new members in 2004 and later with the second “wave†of members in 2007 brought new opportunities for the countries in Eastern Europe, being obvious that “effective utilisation of EU support can foster the success of their economic performance†. Financing investment projects proposed by the SMEs can be realized through several financial sources internal and external, from which two of the most common external sources constitute from subsidies-grants and through banking system. One of the most important financing programme present in Romania, available for SMEs is SOP IEC, which awards grants for investments proposed by production companies. This financing program encourages its beneficiaries to combine the European grant with private funds mostly provided through the banking system. The paper analyzed the methodology used in Cost Benefit Analysis and also The ACB-MININD software which is compulsory to be used for the CBA analysis of these investment projects links for the first time elements of analysis used by the European Commission with elements of analysis used by the banking system. In this context the present article tries to analyze how the ACB-MININD software links the main elements of Cost-Benefit analysis such as NPV or IRR with elements of the bankability software which are mainly used by the banking system such as: cash-flow analysis, loan/interest payment and financial sustainability. Also are presented the links between the banking sector and the external financing sector in the field on investment projects. As final conclusions of this paper we will demonstrate the theoretical and practical role of cost-benefit analysis - financial component to select the best applications that will be proposed for funding under the European grant programs and to link them to the banking system in order to ensure a proper co-financing for these investment projects.