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Forecasting the future movement of yield curves contains valuable information for both academic and practical issues such as bonding pricing, portfolio management, and government policies. The purpose of this paper is to develop a dynamic factor approach that can provide more precise and consistent forecasting results under various yield curve dynamics.

The paper develops a unified dynamic factor model based on Diebold and Li (2006) and Nelson and Siegel (1987) three-factor model to forecast the future movement yield curves. The authors apply the state-space model and the Kalman filter to estimate parameters and extract factors from the US yield curve data.

The authors compare both in-sample and out-of-sample performance of the dynamic approach with various existing models in the literature, and find that the dynamic factor model produces the best in-sample fit, and it dominates existing models in medium- and long-horizon yield curve forecasting performance.

The authors find that the dynamic factor model and the Kalman filter technique should be used with caution when forecasting short maturity yields on a short time horizon, in which the Kalman filter is prone to trade off out-of-sample robustness to maintain its in-sample efficiency.

Bond analysts and portfolio managers can use the dynamic approach to do a more accurate forecast of yield curve movements.

The enhanced forecasting approach also equips the government with a valuable tool in setting macroeconomic policies.

The dynamic factor approach is original in capturing the level, slope, and curvature of yield curves in that the decay rate is set as a free parameter to be estimated from yield curve data, instead of setting it to be a fixed rate as in the existing literature. The difference range of estimated decay rate provides richer yield curve dynamics and is the key to stronger forecasting performance.

This article reviews the extensive history of dynamic performance research, with the goal of providing a clear picture of where the field has been, where it is now, and where it needs to go. Past research has established that job performance does indeed change, but the implications of this dynamism and the predictability of performance trends remain unresolved. Theories are available to help explain dynamic performance, and although far from providing an unambiguous understanding of the phenomenon, they offer direction for future theoretical development. Dynamic performance research does suffer from a number of methodological difficulties, but new techniques have emerged that present even more opportunities to advance knowledge in this area. From this review, I propose research questions to bridge the theoretical and methodological gaps of this area. Answering these questions can advance both research involving job performance prediction and our understanding of the effects of human resource interventions.

Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.

The purpose of this paper is to investigate the state of the art in static and dynamic games (or inter‐firm relationships). This research area has changed significantly over the last 25 years through the development of phenomena such as the supply chain and the progressive overcoming of monopolistic and oligopolistic frameworks. By exploring the state of the art in inter‐firm relationships, this paper aims to identify the most suitable research methods to be used by future research in this domain and to highlight the major areas under investigation.

This research adopts both qualitative and quantitative approaches. The qualitative approach describes the technical differences between static and dynamic methods and gives evidence of their appropriateness when applied to a game. Quantitative analysis transforms some of the information extracted from the qualitative analysis into categorical variables in order to obtain an indication of the major issues still to be addressed.

The resulting findings identify the extent of the use of static and dynamic modelling in previous research and how their use has changed over time, what resolution methods need to be applied when investigating inter‐firm relationship, what features influence this decision and what research areas still remain unexplored.

The existing literature on the modelling of static and dynamic games lacks an exhaustive review. Several contributions investigate the literature on inter‐firms relationships and review numerous issues, but focus only on static or dynamic modelling. This paper fills this gap by reviewing a number of theoretical papers.

The purpose of this paper is to investigate dynamic food demand in urban China, with use of a complete dynamic demand system – dynamic linear expenditure system-linear approximate dynamic almost ideal demand system (DLES-LA/DAIDS), which pushes forward the techniques of demand analysis.

The authors employ a transitionary demand process and develop a new approach of complete demand system with a two-stage dynamic budgeting: a strongly separable DLES in the first stage and a LA/DAIDS in the second stage. Employing provincial aggregate data (1995-2010) from the China urban household surveys, The authors estimated the demand elasticities for primary food products in urban China.

The results indicate that most primary food products are necessities and price inelastic for urban households in China. The authors also found that the dynamic model tends to yield relatively smaller expenditure elasticities in magnitude than the static models do due to the friction effect of dynamic adjusting costs, such as habit formation, switching costs, and learning process. However, the dynamic effects on own price elasticities are inconclusive due to the add-up restriction.

The research contributes to the demand analysis methodologically, and can be used for better projections in policy simulation models.

This paper methodologically relaxes the restrictive assumption of instant adjustment in static models and allows consumers to make a dynamic decision in food consumption. Empirically, the authors introduce a new complete dynamic demand model and carry out a case study with the use of urban household data in China.

The purpose of this paper is to present the development and validation of a methodology which allows modeling and solving the inverse and direct dynamic problem in real time in robot manipulators.

The robot dynamic equation is based on the Gibbs‐Appell equation of motion, yielding a well‐structured set of equations that can be computed in real time. This paper deals with the implementation and calculation of the inverse and direct dynamic problem in robots, with an application to the real‐time control of a PUMA 560 industrial robot provided with an open control architecture based on an industrial personal computer.

The experimental results show the validity of the dynamic model and that the proposed resolution method for the dynamic problem in real time is suitable for control purposes.

The accuracy of the applied friction model determines the accuracy of the identified overall model and consequently of the control. This is especially obvious in the case of the PUMA 560 robot, in which the presence of friction is remarkable in some of their joints. Hence, future work should focus on identifying a more precise friction model. The robot model could also be extended by incorporating rotor dynamics and could be applied for different robot configurations as parallel robots.

Gibbs‐Appell equations are used in order to develop the robotic manipulator dynamic model, instead of more usual dynamics formulations, due to several advantages that these exhibit. The obtained non‐physical identified parameters are adapted in order to enable their use in a control algorithm.

This article provides an overview of the panel vector autoregressive models (VAR) used in macroeconomics and finance to study the dynamic relationships between heterogeneous assets, households, firms, sectors, and countries. We discuss what their distinctive features are, what they are used for, and how they can be derived from economic theory. We also describe how they are estimated and how shock identification is performed. We compare panel VAR models to other approaches used in the literature to estimate dynamic models involving heterogeneous units. Finally, we show how structural time variation can be dealt with.

The purpose of this paper is to enhance the existing socioemotional wealth (SEW) theory. Particularly, the current research proposes a dynamic SEW model using insights from prospect theory. The application of the proposed dynamic SEW model leads to several propositions that will reveal the relationship amongst family business, transgenerational succession, business risks and diversification strategy.

This study is mainly a theoretical research. A dynamic SEW model is proposed in which the SEW is not static and can be increasing or decreasing. SEW is framed as a gain or loss under different scenarios, and the shift in reference point will change the framing or value of SEW.

The current research presents several interesting propositions based on the dynamic SEW model. Generally, family firms are less likely to diversify than non-family firms. However, when family firms face business risks, they are more likely to diversify than those that do not face business risks. Family firms with second generation involvement in management are more likely to diversify than those without second generation involvement. The dynamic SEW model can also be applied to analyse R&D and IPO underpricing for family firms.

This study builds a dynamic SEW model, which is totally new to the literature. The conceptual framework that reveals the relationships amongst family business, transgenerational succession, business risks and diversification strategy also contributes to the literature and has empirical implications to researchers, policy makers and family business owners.

The purpose of this paper is to establish a simplified model of the closed hydrostatic guideway for the rapid analysis of static and dynamic characteristics. Further, the influence of compressibility and dynamic frequency are taken into consideration in the new dynamic model.

The new model is based on the second kind of Lagrange equation. In this model, the closed hydrostatic guideway is supported by 12 pads, and each oil pad is equivalent to a nonlinear spring-damper system. The equivalent spring coefficient and damper coefficient of the oil pad are extracted by the three different equivalent methods. Finally, the validation experiments of step load response and dynamic stiffness are conducted on a hydrostatic guideway.

For solving the step response, the linear spring-damper model and the nonlinear spring-damper Model 1 are better than the nonlinear spring-damper Model 2. The accuracy of the three methods are very high for static stiffness calculation. For the calculation of dynamic stiffness, the nonlinear spring-damper Model 2 is better than the nonlinear spring-damper Model 1. The linear spring-damper model has low precision for dynamic stiffness calculation, especially at high frequency. The accuracy of the new model is validated by experiments.

The equivalent method of nonlinear spring-damper system has higher accuracy. Different equivalent methods should be adopted for different load types. The computational speeds of the new dynamic model with the three methods are much better than finite element method (about ten times).