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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 implied volatility surface is the collection of volatilities implied by option contracts for different strike prices and time-to-maturity. We study factor models to capture the dynamics of this three-dimensional implied volatility surface. Three model types are considered to examine desirable features for representing the surface and its dynamics: a general dynamic factor model, restricted factor models designed to capture the key features of the surface along the moneyness and maturity dimensions, and in-between spline-based methods. Key findings are that: (i) the restricted and spline-based models are both rejected against the general dynamic factor model, (ii) the factors driving the surface are highly persistent, and (iii) for the restricted models option Δ is preferred over the more often used strike relative to spot price as measure for moneyness.

We generalise the spectral EM algorithm for dynamic factor models in Fiorentini, Galesi, and Sentana (2014) to bifactor models with pervasive global factors complemented by regional ones. We exploit the sparsity of the loading matrices so that researchers can estimate those models by maximum likelihood with many series from multiple regions. We also derive convenient expressions for the spectral scores and information matrix, which allows us to switch to the scoring algorithm near the optimum. We explore the ability of a model with a global factor and three regional ones to capture inflation dynamics across 25 European countries over 1999–2014.

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.

Based on the Jones (1971) model, we construct two dynamic models of international trade in which the rate of time preference is either constant or time-varying. The main purpose is to study whether and under what conditions the results derived in the Jones model still hold in the dynamic framework. It is shown that the results of dynamic models may be similar or different to those obtained in the static model. For example, it is possible that, in both static and dynamic models, an increase in the commodity price raises this commodity's output and the return to the specific factor in this sector. However, the effects on the wage rate may be different due to the factor accumulation impact in the dynamic framework.

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.

In the context of Dynamic Factor Models, we compare point and interval estimates of the underlying unobserved factors extracted using small- and big-data procedures. Our paper differs from previous works in the related literature in several ways. First, we focus on factor extraction rather than on prediction of a given variable in the system. Second, the comparisons are carried out by implementing the procedures considered to the same data. Third, we are interested not only on point estimates but also on confidence intervals for the factors. Based on a simulated system and the macroeconomic data set popularized by Stock and Watson (2012), we show that, for a given procedure, factor estimates based on different cross-sectional dimensions are highly correlated. On the other hand, given the cross-sectional dimension, the maximum likelihood Kalman filter and smoother factor estimates are highly correlated with those obtained using hybrid procedures. The PC estimates are somehow less correlated. Finally, the PC intervals based on asymptotic approximations are unrealistically tiny.

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.

We propose a novel dynamic factor model to characterise comovements between returns on securities from different asset classes from different countries. We apply a global-class-country latent factor model and allow time-varying loadings. We are able to separate contagion (asset exposure driven) and excess interdependence (factor volatility driven). Using data from 1999 to 2012, we find evidence of contagion from the US stock market during the 2007–2009 financial crisis, and of excess interdependence during the European debt crisis from May 2010 onwards. Neither contagion nor excess interdependence is found when the average measure of model implied comovements is used.