Term Time Horizon Research Articles

Міжнародна інвестиційна привабливість українських регіонів

The growing volatility of the exogenous and endogenous environment in which economic agents operate causes increased competition for limited investment resources. A further challenge today is the need to stimulate socioeconomic development in Ukraine and strengthen the capacity of its regions to fulfill their social and infrastructural burden in the context of relations with key stakeholders, primarily local communities and businesses. The meaning of key in this context is to set up an adequate system at the micro and macro levels to support the post-war reconstruction of Ukraine’s regions. This situation actualizes the problematics of building an adequately functioning complex of investment attractiveness of the regions. The article is aimed at analyzing the essence of investment attractiveness of regions in the plane of identifying the main drivers of influence on this economic category and forming proposals for its strengthening both on the short and long term time horizon. The article studies the essence of the economic category of «investment attractiveness of region» as a key component of stimulating socioeconomic development. A set of key factors influencing the status and prospective dynamics of investment attractiveness of the region is studied. An analysis of analytical approaches to measuring the investment attractiveness of the region in the context of current challenges for Ukraine and the needs of post-war reconstruction is carried out. Proposals for the development of a complex of investment attractiveness of the regions of Ukraine in the context of post-war reconstruction are formed. The proposed research may be valuable for specialists and researchers in the analyzed sphere, be useful for private and public companies, business associations, international financial institutions, public authorities. A promising direction of future research is to expand the analysis of drivers influencing the state of investment attractiveness in the context of motivations and needs of the main groups of stakeholders.

A hybrid game theory and system dynamics model to determine optimal electricity generation mix

One criteria for the success in achieving sustainable development is to supply electricity by considering the diversity of generation technology. Also, the consumption of fossil fuels is considered as a major challenge for sustainable development. Relying heavily on fossil resources for electricity generation and the growing trend in emissions is one of the serious problems of developing countries. In this research, the sustainable growth of Iran's electricity industry in the short and long term time horizons is investigated using three game theory models in a system dynamic model. The competition, cooperation and profits of electricity suppliers as well as the government's role are modeled. Carbon dioxide emissions, water and natural gas consumption are also calculated. The results show the amount of carbon dioxide emissions, water and gas consumption in the Stackelberg and cooperation model decrease by 19.3%, 18.4% and 16.5%, respectively, compared to the continuation of the current trend.

Globally-optimal whole body motion planning under nonholonomic constraints using dynamic programming

Robotics is a key enabler for many applications in space including planetary exploration, On-Orbit Servicing (OOS), In-Space Manufacturing (ISM), Active Space Debris Removal (ADR) and deep space exploration. They are usually characterized by limited resources, which requires the robotic system to be controlled according to criteria of optimality. How to perform operations and plan the robot motion are then key factors to ensure feasibility and efficiency. This work analyzes and extends a recently proposed Dynamic Programming Optimization (DPO) algorithm for off-line motion planning in the context of planetary colonization, ranging from exploration to terrain preparation and construction. The algorithm is designed to be run on ground with the aim of (1) limiting spacecraft cost and (2) optimally plan complex tasks with medium to long term time horizons before or during mission. The analysis is performed on a robotic system including a six degree of freedom manipulator mounted on a nonholonomic base which is exercised on tasks that leave more than one degree of redundancy. The planning tasks are resolved without decomposition, allowing the algorithm to optimize the motion of the whole kinematic structure. Redundancy is exploited to optimize the cost function of interest, while considering any constraint the robotic asset and the surrounding environment are characterized by. In our use case, the optimization is performed on a cost function associated to energy consumption. A tradeoff analysis of the solution quality versus the computational resources requirements is provided. Finally, we highlight the advantages of the proposed algorithm with respect to a standard planning approach based on decomposition of the planning problem in two sub-problems: the first involving the planning of the mobile base according to some heuristics (e.g. reachability) and the second involving the planning of the manipulator.

Varietal susceptibility overcomes climate change effects on the future trends of rice blast disease in Northern Italy

CONTEXTRice blast manifests with different symptoms according to the affected plant organ, i.e., leaf and panicle blast, the latter being the primary determinant of yield losses in Italy. Forecasting models have been developed to drive fungicides application and perform scenario analyses to plan legislative measures on plant protection. An assessment of the blast pathogen pressure evolution in the Italian rice area under climate change realizations has not been done yet. OBJECTIVEWe developed the ELLE process-based forecasting model (ricE bLast simuLation modEl) to reproduce the dynamics of leaf and panicle blast severity and incidence to release a tool to perform in-season monitoring and scenario assessment. The forecasting model is delivered as a RESTful API and is ready to be used by third parties for operational monitoring activities. Here we present its application to assess future rice blast impacts in Italy after an extended calibration and evaluation with ground-truth data. METHODSThe reference dataset for model testing comprised (a) a multi-year (2013–2015) and multi-site (three locations) experimental trial, where leaf and panicle blast were assessed on four Italian rice varieties with different levels of susceptibility to blast disease, and (b) historical disease data collected at the municipality level. We then performed spatially distributed simulations at 2 × 2 km resolution on the Northern Italian rice district, considering a short- (2030) and long- (2070) term time horizon and rice varieties representative of the current variability in phenology and susceptibility to blast disease within the Italian varietal landscape. The factorial combinations of climatic projections from four General Circulation Models and two extreme CO2 Representative Concentration Pathways were used as model inputs in the scenario analysis. RESULTS AND CONCLUSIONSModel calibration and evaluation at the field level denoted an overall accurate reproduction of leaf blast severity (RRMSE <13%) and panicle blast incidence (RRMSE <17%). The climate change scenario analysis highlighted the varietal susceptibility as the critical factor determining leaf and panicle blast variability in the future, with no significant changes in 2030 and a slight decline in 2070. SIGNIFICANCEBesides delivering a reusable methodology to analyze the severity and incidence of leaf and panicle blast, our modelling study confirms that varietal susceptibility is the main feature to be pursued in rice breeding programs to counteract blast disease.

Empirical Evidence of Co-Movement between the Canadian CDS, Stock Market And TSX 60 Volatility Index

Purpose- The prime objective of this study was to find the co-movement between the Canadian credit default swaps market, the Stock market and volatility index (TSX 60 Index) Design/ Methodology- To achieve this purpose, daily data containing 2870 observations starting from the 1st of January 2009 to the 30th of December 2019 were analyzed. This study employed the wavelet approach to present results in short-term, medium-term, long-term, and very long time. Findings- The findings of this study showed a negative correlation between the CDS market, stock market, and the TSX 60 index in the short-term as well as in the long-term term, while in medium-term and very long-term period correlation is strongly positive. The wavelet co-movement results in the short-term and long-term were negative, while this relationship in the medium-term and very long-term period was strongly positive. Practical Implications- This research provides simultaneous valuable information for investment decisions in the short, medium, and long term time horizons, as well as for the policymakers in the Canadian credit default swaps market, stock market, and the volatility index (TSX 60 Index).

Modelling alternative futures of global food security: Insights from FOODSECURE

Global economic models have been increasingly used to project food and agricultural developments for long term-time horizons, but food security aspects have often been limited to food availability projections. In this paper, we propose a broader framework to explore the future of food and nutrition security with a focus on food availability, food access, and a reasonable proxy for food utilisation. This framework is applied to a new set of stakeholder-designed scenarios of alternative future worlds that were developed for the FOODSECURE project and are structured around the two dimensions of inequality and sustainability. The framework is tested with two global models, MAGNET-IMAGE and GLOBIOM, and illustrated through an assessment of the possible trade-offs between food and nutrition security and sustainability in each of the worlds. Our results indicate that more equal worlds improve food security over a wider range of food security indicators and neglecting the sustainability dimension might revert food security gains over time. This paper concludes that there is a need for model-based scenario analysis to assess the complex and multi-dimensional characteristics of global food security.

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.

Multiperiod optimization model for oilfield production planning: bicriterion optimization and two-stage stochastic programming model

In this work, we present different tools of mathematical modeling that can be used in oil and gas industry to help improve the decision-making for field development, production optimization and planning. Firstly, we formulate models to compare simultaneous multiperiod optimization and sequential single period optimization for the maximization of net present value and the maximization of total oil production over long term time horizons. This study helps to identify the importance of multiperiod optimization in oil and gas production planning. Further, we formulate a bicriterion optimization model to determine the ideal compromise solution between maximization of the two objective functions, the net present value (NPV) and the total oil production. To account for the importance of hedging against uncertainty in the oil production, we formulate a two-stage stochastic programming model to compute an improved expected value of NPV and total oil production for uncertainties in oil prices and productivity indices.

The Effect of Prior Price Trends on Optimistic Forecasting

Purpose - The purpose of this study examines when the optimism impact on financial asset price forecasting and the boundary condition of optimism in the financial asset price forecasting. People generally tend to optimistically forecast their future. Optimism is a nature of human beings and optimistic forecasting observed in daily life. But is it always observed in financial asset price forecasting? In this study, two factors were focused on considering whether the optimism that people have applied to predicting future performance of financial investment products (e.g., mutual fund). First, this study examined whether the degree of optimism varied depending on the direction of the prior price trend. Second, this study examined whether the degree of optimism varied according to the forecast period by dividing the future forecasted by people into three time horizon based on forecast period. Research design, data, and methodology - 2 (prior price trend: rising-up trend vs falling-down trend) &#x00D7; 3 (forecast time horizon: short term vs medium term vs long term) experimental design was used. Prior price trend was used between subject and forecast time horizon was used within subject design. 169 undergraduate students participated in the experiment. &#x03C7;² analysis was used. In this study, prior price trend divided into two types: rising-up trend versus falling-down trend. Forecast time horizon divided into three types: short term (after one month), medium term (after one year), and long term (after five years). Results - Optimistic price forecasting and boundary condition was found. Participants who were exposed to falling-down trend did not make optimistic predictions in the short term, but over time they tended to be more optimistic about the future in the medium term and long term. However, participants who were exposed to rising-up trend were over-optimistic in the short term, but over time, less optimistic in the medium and long term. Optimistic price forecasting was found when participants forecasted in the long term. Exposure to prior price trends (rising-up trend vs falling-down trend) was a boundary condition of optimistic price forecasting. Conclusions - The results indicated that individuals were more likely to be impacted by prior price tends in the short term time horizon, while being optimistic in the long term time horizon.

Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms

This paper evaluates the performance of deep recurrent neural networks in predicting heating demand for a commercial building over a medium-to-long term time horizon (⩾1 week), and proposes a modeling framework to demonstrate how these longer-term predictions can be used to aid design of a stratified thermal storage tank. The building sector contributes significantly to primary energy consumption in the US, and as such, there is a need to predict heating demand in buildings over longer time horizons, and to develop methods that can facilitate installation, planning and management of distributed generation and thermal storage to meet these heating demands. Key objectives of this paper are: (a) Investigate how a deep recurrent neural network model performs in predicting heating demand in campus buildings at University of Utah over multiple weeks, and (b) develop an optimization framework that which can provide definitive guidelines on sizing a stratified thermal storage tank without requiring high performance computing resources. The results showed that the predictions by the deep RNN are comparatively more accurate than those by a 3-layer MLP, and that these deep RNN predictions can adequately serve as proxy for future demand while considering sizing in the design of a complementary stratified thermal storage tank.

Global challenges and trends in agriculture: impacts on Russia and possible strategies for adaptation

PurposeThis paper aims to analyse the mainstream and emerging global challenges and trends in the global agriculture sector. The analysis leads to a discussion on the present state of the Russian agroindustry and possible future strategies for adaptation in the context of the rapidly changing global environment.Design/methodology/approachThe design of this study is based on the application of the core methods of Foresight. First, a trend analysis is undertaken using reviews and expert methods. Trends identified are mapped using a social, technological, economic, environmental, political and value (STEEPV) framework to ensure that a broad range of trends are covered, which may be stemming from various factors affecting the agriculture sector. The analysis of the big picture of global trends and challenges, interacting with country-specific structural factors, translates are translated into the opportunities and threats, which will in turn help to develop possible strategies for adaptation.FindingsThis study develops two adaptive strategies for the development of the Russian agroindustry that are feasible in different short- and long–term time horizons. The first strategy is considered to be the most likely choice for the period before 2020. It includes radical imports’ substitution (of commodities as well as machinery and high-tech components) for ensuring national food security with inevitable temporary setbacks in efficiency and labour productivity. The second strategy, which becomes feasible after 2020, considers re-integrating Russia into global supply chains and expanding commodities exports (volumes and nomenclature) based on full-scale technological modernization with the use of international capital.Research limitations/implicationsThe study design is based on the assumption that Russia’s position as a country, which is highly self-sufficient on basic agricultural products and large exporter of crop commodities and fertilizers, will remain unchanged in the horizon of at least 20 years. However, long-term forecasts should also scrutinize the possibility of radical structural changes. Therefore, future research should concentrate on wild cards that can completely disrupt and transform the Russian agriculture industry and as well as the whole economy.Practical implicationsThis paper suggests a number of recommendations on national science and technology policy for the three main industries of the Russian agricultural sector: crop husbandry, animal breeding and food processing (the fisheries sector is excluded from the scope of this paper). In addition, this paper proposes a number of measures towards alleviating the institutional barriers to raise the investment attractiveness of the sector.Originality/valueThe novelty of this paper lies in the originality of the research topic and methodology. The Russian agricultural sector has rarely been studied in the context of global agricultural challenges and threats taken on the highest level of aggregation beyond commodity market analysis or agro-climatic and logistics factors. There are few or no studies that lay out a map of possible long-term strategies of Russian agroindustry adaptive development. The Foresight methodology applied in this study is customized to better fit the practical purposes of the study.

Term limits, time horizons and electoral accountability

Term limits have been known to reduce electoral accountability by removing the possibility of reelection, thus affecting economic policy choices (i.e., the ‘lame duck’ effect). We show that the magnitude and statistical significance of this effect is influenced by the expected length of a future career. By using incumbent age as a proxy for expected career length, we find that the lame-duck effect is statistically observable only among those politicians with long careers ahead. Using data on US governors from 1950 to 2005, we find evidence that the influence of term limits is heterogeneous, primarily influencing young incumbents who hope to have long careers and thus have stronger incentives to remain accountable to voters. Indeed, we find little evidence of a lame-duck effect among older incumbents, suggesting that their already-shortened time horizons may offset the term limit effect.

Технологічне передбачення економіки України на середньостроковому (до 2020 р.) і довгостроковому (до 2030 р.) часових горизонтах

Технологічне передбачення економіки України на середньостроковому (до 2020 р.) і довгостроковому (до 2030 р.) часових горизонтах

Co-movement of oil price, exchange rate and stock index of major oil importing countries: A wavelet coherence approach

The study examines the co-movement between oil price and macroeconomic indicators such as exchange rate and stock indices of major oil importing countries. We differ from previous studies by examining the macroeconomic dynamics of major oil-importing countries during all economic cycles across different frequencies, using wavelet coherence analysis. We use nominal price rather than real price to make the results more meaningful for traders and institutional investors. Our analysis is based on 3012 observations covering the period 2003-14 for fifteen major oil importing countries. Wavelet coherence analysis indicates a high coherence between oil price and macroeconomic indicators across all the countries during the financial crisis. The nominal exchange rates tend to have a negative relationship with benchmark oil prices except Japan in the long run and South Korea in the medium run. Stock indices tend to have a positive relationship with benchmark oil prices in both long and medium run. S&P is leading the oil price, whereas SSE50, Nikkei 225, NIFTY, KOPSI, DAX, CAC, IBEX, FTSSI, FTSEMIB, AEX, TWSE, XU 100, LQ45 and BEL 20 are lagging the oil price in the long run. In the medium term, except for NIFTY, oil price is leading the stock market index. Overall, the results indicate that the oil price and stock indices of the major oil-importing countries are correlated in the long and medium term, but not in the short term. The lead–lag relationship between oil price and macroeconomic indicators are observed to change across frequency and time. While exchange rate offers diversification benefits, stock market indices provide no diversification avenues, since the pattern of co-movement of stock market indices and oil prices are similar across all oil importing countries. The results have implications for individual traders and institutional investors while designing their portfolio for short, medium and long term time horizons.

Sensitivity Analysis of a Land-Use Change Model with and without Agents to Assess Land Abandonment and Long-Term Re-Forestation in a Swiss Mountain Region

Land abandonment and the subsequent re-forestation are important drivers behind the loss of ecosystem services in mountain regions. Agent-based models can help to identify global change impacts on farmland abandonment and can test policy and management options to counteract this development. Realigning the representation of human decision making with time scales of ecological processes such as reforestation presents a major challenge in this context. Models either focus on the agent-specific behavior anchored in the current generation of farmers at the expense of representing longer scale environmental processes or they emphasize the simulation of long-term economic and forest developments where representation of human behavior is simplified in time and space. In this context, we compare the representation of individual and aggregated decision-making in the same model structure and by doing so address some implications of choosing short or long term time horizons in land-use modeling. Based on survey data, we integrate dynamic agents into a comparative static economic sector supply model in a Swiss mountain region. The results from an extensive sensitivity analysis show that this agent-based land-use change model can reproduce observed data correctly and that both model versions are sensitive to the same model parameters. In particular, in both models the specification of opportunity costs determines the extent of production activities and land-use changes by restricting the output space. Our results point out that the agent-based model can capture short and medium term developments in land abandonment better than the aggregated version without losing its sensitivity to important socio-economic drivers. For comparative static approaches, extensive sensitivity analysis with respect to opportunity costs, i.e., the measure of benefits forgone due to alternative uses of labor is essential for the assessment of the impact of climate change on land abandonment and re-forestation in mountain regions.

A Case for Dynamic Asset Allocation for Long Term Investors

Abstract The strategic asset allocation (SAA) decision represents the key driver of results for long term institutional investors. In practice this decision translates into a static portfolio with fixed allocations to narrowly defined asset classes which is not responsive to time varying expected return and risk driven by changes in the economic environment for example. In this context we define dynamic asset allocation (DAA) as the process of implementing time varying expected excess returns (returns over the risk free rate) and/or expected risk with a medium term time horizon. In this paper we assess its applicability for long term institutional investors, review different forms of implementation seen in practice, and discuss key requirements and governance challenges in an institutional context.

Infrastructure transformation as a socio-technical process — Implications for the governance of energy distribution networks in the UK

This paper seeks to uncover and examine the complex set of governance challenges associated with transforming energy distribution networks, which play a key enabling role in a low carbon energy transition. We argue that, although the importance of such infrastructure networks to sustainability and low carbon transitions in the energy, water and mobility sectors is clear, there is relatively little understanding of the social and institutional dimension of these systems and appropriate governance strategies for their transformation. This may be because the prevalent model of infrastructure governance in the energy and other sectors has prioritised short term time horizons and static efficiencies. In this paper we draw on the social shaping of technology literature to develop a broader understanding of infrastructure change as a dynamic socio-technical process. The empirical focus of the paper is on the development of more flexible and sustainable energy distribution systems as key enablers for the UK's low carbon transition. Focusing on electricity and heat networks we identify a range of governance challenges along different phases of the ‘infrastructure lifecycle’, and we draw lessons for the development of governance frameworks for the transformation of energy infrastructure more generally.

Health-Economic Comparison of Sensor-Augmented Pump With Low Glucose Suspend Versus Insulin Pump Alone For The Treatment Of Hypo-Prone Type 1 Diabetes In Slovakia.

To project the long-term costs and outcomes of sensor-augmented pump (SAP) with low glucose suspend (LGS) versus insulin pump alone (CSII) for the treatment of hypo-prone Type 1 diabetes in Slovakia. The CORE Diabetes Model is a peer-reviewed, validated model, which employs standard Markov/Monte Carlo simulation techniques to describe the long-term incidence and progression of diabetes-related complications. It was used to simulate disease progression in a cohort of patients with baseline characteristics (mean age 18.6 years, duration of diabetes 12 years, mean HbA1c 7.5%) and clinical outcomes (severe hypoglycaemic event rates; Quality of Life; HbA1c) taken from a recent randomised controlled trial (Ly et al, 2013). Local treatment and complication cost data was used. The main scenario considered in this cost-effectiveness analysis was the comparison of sensor-augmented insulin pump (SAP) with low glucose suspend (LGS) versus insulin pump alone (CSII). The target population was type I hypo-prone diabetes patients with the analysis based on a deterministic microsimulation of 1,000 patients, using a 5 year time horizon. Direct costs were calculated from a third-party payer perspective. Discount rates of 3% per annum were applied to both costs and clinical outcomes. The Incremental-Cost-Effectiveness-Ratio (ICER) for SAP+LGS (vs CSII) was €17,893 per Quality-Adjusted-Life-Year gained over a 5 year time horizon. Results were similar across a 1 to 10 year time horizon. Other extensive sensitivity analyses showed the robustness of the results. Using a payer’s perspective, our analysis showed that SAP (w LGS) is cost-effective over a short term (5 year) time horizon in hypo-prone Type 1 Diabetes patients in Slovakia (using a WTP threshold of 1x [€18,000] or 3x [€54,000] Slovakia GDP).

The Performance of Indian Capital Market: A Critical Analysis

The capital market plays the role of intermediary where funds can be borrowed and lent for long term time horizon or alternatively it can be said that capital market is a mechanism where market forces such as demand and supply for long term fund interact with each other. Indian equity capital market is undergoing through a strong bullish phase and the Sensitivity Index of Mumbai Stock Exchange has crossed 27000 points in the first week of September, 2014 which is its lifetime high. In order to identify the causes for sudden sustained upsurge of the equity market, the background story should be analysed properly.

Forecasting Crude Oil Price with Multiscale Denoising Ensemble Model

Crude oil price becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulations worldwide. Current methodologies are being challenged as they have been constrained by traditional approaches assuming homogeneous time horizons and investment strategies. Approximations they provided over the long term time horizon no longer satisfy the accuracy requirement at shorter term and more microlevels. This paper proposes a novel crude oil price forecasting model based on the wavelet denoising ARMA models ensemble by least square support vector regression with the reduced forecasting matrix dimensions by independent component analysis. The proposed methodology combines the multi resolution analysis and nonlinear ensemble framework. The wavelet denoising based algorithm is introduced to separate and extract the underlying data components with distinct features, corresponding to investors with different investment scales, which are modeled with time series models of different specifications and parameters. Then least square support vector regression is introduced to nonlinearly ensemble results based on different wavelet families to further reduce the estimation biases and improve the forecasting generalizability. Empirical studies show the significant performance improvement when the proposed model is tested against the bench-mark models.