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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 chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

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.

This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.

First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.

Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.

This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

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.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

Our theoretical understanding of subjective well-being in the workplace is incomplete without a dynamic understanding of antecedents and outcomes of subjective well-being. While between-person differences provide useful information about employee outcomes, these differences do not provide information about the relationships between subjective well-being and employee outcomes that evolve over time and across situations. In this paper, we discuss specific statistical methods within the nomothetic and idiographic perspectives that can support dynamic research on subjective well-being in the workplace and outline unanswered contemporary questions regarding structure, processes, and dynamics of subjective well-being that may be addressed with these methods reviewed; some of which were proposed in early research but progressed slowly due to a lack of adequate methods. This discussion highlights how idiographic methods from outside organizational psychology can be applied to the study of worker subjective well-being to strengthen this dynamic approach in a way that addresses limitations associated with reliance on between-person models.