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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.

The purpose of this study is to achieve the dynamic model of outsourcing success factors in project-based companies.

This study is descriptive-survey in terms of method and practical in terms of purpose. To achieve the dynamic model of outsourcing success, 1,000 outsourcing articles published in high-status journals from 2017 to 2019 were first text-mining. Then, using the clustering technique, the factors affecting the success of outsourcing were obtained. To achieve the key variables, the variables obtained by interpretive structural modeling (ISM) were then leveled. Then, the strategic options development and analysis (SODA) technique has been used to achieve a consensus and coordination on factors relationships. Finally, the dynamic model of outsourcing success in GHODRAT CONTROL PARS Company has been modeled and implemented.

In total, five clusters and nine factors were extracted (strategy, management, performance, market, R&D, supplier, product, organizational data and outsourcing findings). In central and domain analysis, two factors, “Strategy” and “R and D,” were recognized as factors that have the most interaction and centrality. The result of the dynamic model indicate that the organization will significantly reduce the construction time of the power plant by improving the “R and D” factor.

In this study, various techniques have been combined. Therefore, one of the aspects of innovation in the present study is the combination of methods that have not been used earlier. The second aspect of this study’s innovation is using SODA technique to design the dynamic model of outsourcing success factors. Given that the scope of this study is the component affecting the success of outsourcing, so extensive research has been conducted in the field of articles worked in the field of outsourcing to get a comprehensive view of the components affecting the success of outsourcing, which has not been reviewed in other articles. In this study, in addition to identifying the effective factors, their identified and also how these variables affect the successful performance of outsourcing in the form of a dynamic model, and then analyzed.

This paper aims to investigate the determinants of dynamic efficiency in the Spanish hotel industry. The study also aims to introduce a large number of variables potentially related to efficiency and performance measurement. In particular, it seeks to explore the association between efficiency scores and firm-specific factors (variables related to market conditions, business factors, audit variables, organisational forms and subsidiary variables).

In this study, the data envelopment analysis (DEA) double-frontier approach is used according to firm size in conjunction with non-parametric tests (Mann–Whitney U and Kruskal–Wallis tests), a dynamic Tobit regression model and a bootstrapping procedure. The tests are performed using 1,805 hotels from the years 2002 to 2011. This allows the authors to overcome several of the major limitations of previous papers, namely, the low number of observations, the static or cross-sectional analysis referring to a single period and the use of conventional DEA models, among others.

The results show significant differences in dynamic efficiency among Spanish hotel companies. In addition, the evidence suggests the levels of efficiency are related to the hotel's location, the hotel's size, internationalisation, the first source of the hotel's activity, audit service and management variables.

One limitation of the study is related to the input and output variables specified in the DEA model. The selection of inputs and outputs was based on data availability and the previous literature on hotel efficiency, but the results might change if the hotel sample and the selected input and output variables were changed. Another limitation is the availability of data on ownership structure and subsidiary variables for very small businesses.

The paper contributes to the tourism literature by offering new insights into hotel performance: dynamic efficiency evaluation and its main determinants. The paper presents strategic market implications for hoteliers, government decision-makers and destination management organisations.

This is the first part of a two-part paper. The purpose of this paper is to report on methods that use the Response Surface Methodology (RSM) to investigate an Evolutionary Algorithm (EA) and memory-based approach referred to as McBAR – the Mapping of Task IDs for Centroid-Based Adaptation with Random Immigrants. Some of the methods are useful for investigating the performance (solution-search abilities) of techniques (comprised of McBAR and other selected EA-based techniques) for solving some multi-objective dynamic resource-constrained project scheduling problems with time-varying number of tasks.

The RSM is applied to: determine some EA parameters of the techniques, develop models of the performance of each technique, legitimize some algorithmic components of McBAR, manifest the relative performance of McBAR over the other techniques and determine the resiliency of McBAR against changes in the environment.

The results of applying the methods are explored in the second part of this work.

The models are composite and characterize an EA memory-based technique. Further, the resiliency of techniques is determined by applying Lagrange optimization that involves the models.

The production of a good or service requires that the supplier performs a wide range of activities. Many companies are trying to fit outsourcing with the dimensions of their organization. Outsourcing is increasingly being used among Iranian companies in the field of the electric power industry. Human resources are among the factors that consider outsourcing as an obstacle for their growth because staffs declare that the development of outsourcing is a serious threat for unemployment of them and ultimately expulsion from work. Thus, this study surveys the dynamic effects of human resources and work experience on outsourcing decisions. This paper aims to propose a model for dynamic strategic outsourcing focusing on human resource and work experience.

Presenting a model for strategic outsourcing can help organizations to resolve their outsourcing problems. The data needed for examining the dynamic impact of human resources and work experience on outsourcing have been compiled using library and field studies. The method used in this study is an integrated approach, so the model could consider the general effects of manpower and present a systematic view. After interviewing with experts in power industry, the causal relationships of the variables were determined, and a dynamic model based on the applications of the dynamic system was developed in VENSIM software. The research model will be completed in a three-year period (2016-2019) in the power industry of Iran.

For completing projects in an organization, cooperation between internal employees and external contractors is needed; thus, results based on both external contractors and internal staff have shown that outsourcing through working of a number of contractors will be more effective than frequent use of one contractor. On the other hand, improving the quality of projects could be done by training new employees using skilled and expert employees.

Existence of one-dimensional models (only with qualitative factors or only with quantitative factors) in the context of outsourcing in the past studies has prompted to study different types of factors together as a dynamic model. This paper presents a quantitative and qualitative model in the field of strategic outsourcing with emphasis on human resources and work experience. In the past studies, there was no way to formulate the qualitative factors, and they simply used the data from the organization, and the only formulation in their works was based on quantitative factors. But in this study, both factors with dynamic modeling have been formulated.

The purpose of this paper is to investigate investor behavior under two broad categories, market-wide sentiment and herding.

Using a dynamic factor model, that extracts distinct latent factors representing fluctuations in asset returns due to changes in fundamentals as well as investors’ sentiments, the paper investigates the impact of investor behavior on asset pricing.

Consistent with the literature, the results suggest that the behavioral factors play a significant role in explaining variation in the asset prices. However, the degree of influence depends on the nature of the stocks or portfolios. The findings conform to the hypothesis that behavioral factors play a more important role in explaining the price movements of high and medium valued stocks than those of smaller valued stocks. Further, the behavioral factors also exhibit high auto-correlation, depicting the pervasive nature of such factors, and proving that information cascades and other behavioral mechanisms propagate over a period of time leading to bubbles and market crashes. Finally, since herding is often associated with market volatility, the authors test the hypothesis using two measures of volatility and the result shows positive significant associations between them as suggested in the literature.

The paper presents a dynamic factor model to study the impact of investor behavior on asset returns using a conventional three factors model with behavioral factors. A factor model is proposed to extract distinct latent factors representing fluctuations in asset returns due to changes in fundamentals as well as investors’ sentiments. The study investigates investor behavior under two broad categories, market-wide sentiment and herding. Consistent with the literature, the results suggest that the behavioral factors play a significant role in explaining variation in the asset prices. However, the degree of influence depends on the nature of the stocks or portfolios. The findings conform to the hypothesis that behavioral factors play a more important role in explaining the price movements of high and medium valued stocks than those of smaller valued stocks. Further, the behavioral factors also exhibit high auto-correlation, depicting the pervasive nature of such factors, and proving that information cascades and other behavioral mechanisms propagate over a period of time leading to bubbles and market crashes.

This study investigates the dynamic production structure of the Japanese manufacturing industry by using the adjustment cost approach. The study is to shed some light on the unique dynamic structure of the Japanese manufacturing industry. The study attempts to help design and predict industrial policies that are implemented to enhance domestic investments by the Japanese government.

This study obtains a system of dynamic factor demand and output supply equations by applying the dual approach to the intertemporal value function as represented by the Hamilton–Jacobi equation. By using industrial panel data for 1973–2012 of the Japanese manufacturing industry, the study estimates the system of the behavioral equations and corresponding elasticities. The study uses hypothesis tests and dynamic elasticities to investigate the dynamic structure of the Japanese manufacturing industry.

Estimation results show that labor and capital are quasi-fixed variables that adjust about 0.2 percent annually to the long-run optimum levels. Estimated adjustment rates are very slow as often presumed about the Japanese manufacturing industry, which uses lifetime employment practice and slow decision-making process in investment decisions. The results also show that output supply and factor demand elasticities vary greatly depending on time horizon. Factor demand increases when its own price increases in the short run, suggesting that factor adjustment is mostly determined factor prices in the past due to sluggish factor adjustment. However, factor demand becomes a normal downward-sloping curve in the long run as factor adjustment gets completed.

Japanese manufacturing firms hire employees through lifetime contract to exploit the benefits of dynamic learning-by-doing and execute investments carefully considering all the possible impacts. Under the strategy, adjustment costs for changing workers and capital stock are minimized. Dynamic adjustment model is expected to shed some light on the unique dynamic structure of the Japanese manufacturing industry. However, researches regarding the dynamic factor adjustment of the Japanese manufacturing industry are hard to find. This study is expected to fill the research vacuum.

The purpose of the paper is to investigate the global and regional influences on the domestic term structure of nine Asian economies.

The dynamic Nelson Siegel model was used to extract the latent factors of a country’s yield curve movements in a state-space framework using the Kalman filter. The global and regional factors of the yield curve were extracted using the dynamic factor model. Further, the Bayesian inference of Gibbs sampling approach was used to identify the influence of global and regional factors on the domestic yield curve.

The results suggest that financial integration does not reduce the control of monetary authorities on the front end of the yield curve, and long-term interest rate is the potential transmission channel through which the contagion of the financial crisis spreads.

The results of this study would help the monetary authorities to understand the efficacy of the monetary policy transmission mechanism. It also offers the global investors diversification opportunities for investing in the Asian bond markets.

It is one of the earliest attempts to capture the global and regional yield curve movements and their impact on the emerging Asian economies yield curve. It contributes to literature by identifying the linkages in the long-term factor that is the potential channel through which crisis spreads.

The purpose of this paper is to present a new methodology, used for dynamic reliability analysis of a gear transmission system (GTS) of wind turbine (WT), which could be used for assembly decision-making of the parts with errors to improve the GTS’s performance.

This paper involves the dynamic and dynamic reliability analysis of a GTS. The history curves of dynamic responses of the parts are obtained with the developed gear-bearing coupling dynamic model considering the random errors, failure dependency and random load. Then, the surrogate models of the mean and standard deviation of responses are presented by statistics, rain flow counting method and corrected-partial least squares regression response surface method. Further, a novel dynamic reliability model based on the maximum extreme theory, a theory of sequential statistics, equivalent principles and the inverse transform theory of random variable sampling, is developed to overcome the limitations of traditional methods.

The dynamic reliability of GTS considering the different impact factors are evaluated. The proposed reliability methodology not only overcomes the limitations associated with traditional approaches but also provides good guidance to assembly the parts in a GTS to its best performance.

Instead of constant errors, this paper considers the randomness of the impact factors to develop the dynamic reliability model. Further, instead of the limitation of the normal distribution of the random parameters in the traditional method, the proposed methodology can deal with the problems with non-normal distribution parameters, which is more suitable for the real engineering problems.

This study aims to introduce an education sector which transforms a part of unskilled labour into new skilled labour, and then show how the level of output of educational service is determined in the short-run equilibrium along with the level of output of two production sectors. This study also introduces intertemporal dynamics into the model assuming that all factor endowments grow over time, and then show how a strong anti-immigration policy in the destination country affects the long-run equilibrium of the source country.

This study considers a three sector open economy model to analyse the long-run economic effects of the anti-immigration policy adopted in the destination country on the general equilibrium of the source country.

If the education sector in the source country is more skilled labour intensive than the advanced production sector, then this anti-immigration policy would raise the capital unskilled labour ratio, skilled labour–unskilled labour ratio and the balanced endogenous growth rate in the new long-run equilibrium but would lower the gross rate of creation of new skilled labour there.

The authors want to analyse the effect of anti-immigration policy adopted in the destination country on the long-run balanced growth rate in the source country. The dynamic growth effect of anti-immigration policy cannot be studied in a static short-run equilibrium model, the authors also introduce intertemporal dynamics into the model assuming that all factor endowments grow over time and then show how a strong anti-immigration policy in the destination country affects the long-run equilibrium of the source country.