Estimation of two- and three-stage vibration device's parameters with asymmetric oscillations in terms of system's dynamic factor

World economic aggregates are compiled infrequently and released after considerable lags. There are, however, many potentially relevant series released in a timely manner and at a higher frequency that could provide significant information about the evolution of global aggregates. The challenge is then to extract the relevant information from this multitude of indicators and combine it to track the real-time evolution of the target variables. We develop a methodology based on dynamic factor models adapted for variables with heterogeneous frequencies, ragged ends and missing data. We apply this methodology to nowcast global trade in goods in goods and services. In addition to monitoring these variables in real time, this method can also be used to obtain short-term forecasts based on the most up-to-date values of the underlying indicators.

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Adult Education: A Sustainable Model for the Reduction of Psychosocial and Educational Risks Caused by COVID-19

Sustainability ◽

10.3390/su13095264 ◽

2021 ◽

Vol 13 (9) ◽

pp. 5264

Author(s):

Manuel-Jesús Perea-Rodríguez ◽

Juan-Agustín Morón-Marchena ◽

María-Carmen Muñoz-Díaz ◽

David Cobos-Sanchiz

Keyword(s):

Risk Factors ◽

Adult Education ◽

Healthy Aging ◽

Teacher Assessment ◽

Psychosocial Risk Factors ◽

Psychosocial Risk ◽

Educational Activity ◽

Psychological State ◽

Dynamic Factor ◽

New Perspective

Education for health and sustainability has to be understood from a new perspective beyond the traditional conceptual limits. Thus, following the lines of the 2030 sustainable development goals, we examine how permanent education and adult education can become a fundamental element for the achievement of said objectives, serving as a neutraliser of psychosocial risk factors. In other words, a quality education throughout life becomes a dynamic factor for the development of lifestyle habits and healthy aging, purposes that during the pandemic and the state of alarm have been altered by confinement, closure of educational centres, and methodological changes. The objective of the study is to analyse whether the maintenance of educational activity has influenced the psychological state of people, reducing, neutralising, or increasing the psychosocial risk factors linked to confinement and the evolution of COVID-19. For this, an observational study was developed, taking as a case the Universidad Popular Dos Hermanas (Seville, Spain), with a sample of 384 learners over the age of 16 years. The variables considered were sociodemographic means and technical tools, assessment of the institution, teacher assessment, and psychosocial variables related to possible effects caused by the context. The data were collected through a self-developed questionnaire. Descriptive analyses and bivariate correlations were carried out. Methodological diversity and positive correlations were shown in terms of the institution’s function, teaching assessment, maintenance of activity, and reduction of psychopathological risks.

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A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU

Italian Economic Journal ◽

10.1007/s40797-021-00155-2 ◽

2021 ◽

Author(s):

Cosimo Magazzino ◽

Marco Mele

Keyword(s):

Neural Networks ◽

Factor Model ◽

Common Factor ◽

Back Propagation ◽

Dynamic Factor ◽

Common Component ◽

Economic Cycles ◽

Composite Nature ◽

Empirical Demonstration ◽

Public Revenues

AbstractThis paper shows that the co-movement of public revenues in the European Monetary Union (EMU) is driven by an unobserved common factor. Our empirical analysis uses yearly data covering the period 1970–2014 for 12 selected EMU member countries. We have found that this common component has a significant impact on public revenues in the majority of the countries. We highlight this common pattern in a dynamic factor model (DFM). Since this factor is unobservable, it is difficult to agree on what it represents. We argue that the latent factor that emerges from the two different empirical approaches used might have a composite nature, being the result of both the more general convergence of the economic cycles of the countries in the area and the increasingly better tuned tax structure. However, the original aspect of our paper is the use of a back-propagation neural networks (BPNN)-DF model to test the results of the time-series. At the level of computer programming, the results obtained represent the first empirical demonstration of the latent factor’s presence.

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Numerical and experimental study of the dynamic factor of the dynamic load on the urban railway

Journal of the Mechanical Behavior of Materials ◽

10.1515/jmbm-2020-0020 ◽

2020 ◽

Vol 29 (1) ◽

pp. 195-202

Author(s):

Tran Anh Dung ◽

Mai Van Tham ◽

Do Xuan Quy ◽

Tran The Truyen ◽

Pham Van Ky ◽

...

Keyword(s):

Experimental Study ◽

Dynamic Load ◽

Dynamic Measurement ◽

Experimental Results ◽

Load Factor ◽

Dynamic Factor ◽

Experimental Measurements ◽

Train Speed ◽

Dynamic Load Factor

AbstractThis paper presents simulation calculations and experimental measurements to determine the dynamic load factor (DLF) of train on the urban railway in Vietnam. Simulation calculations are performed by SIMPACK software. Dynamic measurement experiments were conducted on Cat Linh – Ha Dong line. The simulation and experimental results provide the DLF values with the largest difference of 2.46% when the train speed varies from 0 km/h to 80 km/h

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Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

Technometrics ◽

10.1198/tech.2011.09050 ◽

2011 ◽

Vol 53 (2) ◽

pp. 137-151 ◽

Cited By ~ 28

Author(s):

Andrés M. Alonso ◽

Carolina García-Martos ◽

Julio Rodríguez ◽

María Jesús Sánchez

Keyword(s):

Factor Analysis ◽

Electricity Market ◽

Seasonal Dynamic ◽

Dynamic Factor Analysis ◽

Dynamic Factor ◽

Bootstrap Inference

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Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

PROMET - Traffic&Transportation ◽

10.7307/ptt.v29i5.2334 ◽

2017 ◽

Vol 29 (5) ◽

pp. 529-542 ◽

Cited By ~ 8

Author(s):

Marko Intihar ◽

Tomaž Kramberger ◽

Dejan Dragan

Keyword(s):

Factor Analysis ◽

Goodness Of Fit ◽

Moving Average ◽

Real Data ◽

Information Criteria ◽

Dynamic Factor Analysis ◽

Forecasting Model ◽

Dynamic Factor ◽

Macroeconomic Indicators ◽

The Impact

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

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