Forecast Combinations in the Presence of Structural Breaks: Evidence from U.S. Equity Markets

Gaetano, Davide De

doi:10.3390/math6030034

Cited by 8 publications

(7 citation statements)

References 46 publications

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“…Ignoring structural breaks can lead to negative implications such as inconsistency of the parameter estimates and forecast failures [ 58 ]. Accordingly, for each structural break, it was decided to fit one model before this event, and another one after the structural break.…”

Section: Methodsmentioning

confidence: 99%

A Methodology for Discriminant Time Series Analysis Applied to Microclimate Monitoring of Fresco Paintings

Ramírez

Zarzo

Perles

et al. 2021

Sensors

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The famous Renaissance frescoes in Valencia’s Cathedral (Spain) have been kept under confined temperature and relative humidity (RH) conditions for about 300 years, until the removal of the baroque vault covering them in 2006. In the interest of longer-term preservation and in order to maintain these frescoes in good condition, a unique monitoring system was implemented to record both air temperature and RH. Sensors were installed at different points at the vault of the apse during the restoration process. The present study proposes a statistical methodology for analyzing a subset of RH data recorded by the sensors in 2008 and 2010. This methodology is based on fitting different functions and models to the time series, in order to classify the different sensors.The methodology proposed, computes classification variables and applies a discriminant technique to them. The classification variables correspond to estimates of model parameters of and features such as mean and maximum, among others. These features are computed using values of functions such as spectral density, sample autocorrelation (sample ACF), sample partial autocorrelation (sample PACF), and moving range (MR). The classification variables computed were structured as a matrix. Next, sparse partial least squares discriminant analysis (sPLS-DA) was applied in order to discriminate sensors according to their position in the vault. It was found that the classification of sensors derived from Seasonal ARIMA-TGARCH showed the best performance (i.e., lowest classification error rate). Based on these results, the methodology applied here could be useful for characterizing the differences in RH, measured at different positions in a historical building.

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Section: Methodsmentioning

confidence: 99%

A Methodology for Discriminant Time Series Analysis Applied to Microclimate Monitoring of Fresco Paintings

Ramírez

Zarzo

Perles

et al. 2021

Sensors

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“…According to the structural breaks, all time series were split into eight stages, and the four methods explained in the next sections were applied to each stage. This step is necessary to avoid possible problems with the properties of the estimated parameters of the models applied [ 94 ]. The four methods (M1–M4) were carried out separately for each stage of the 27 time series of

: Wr1A, Wr1B, CdA, CdB, Tr, HtA, HtB and Wr2.…”

Section: Resultsmentioning

confidence: 99%

Multivariate Time Series Analysis of Temperatures in the Archaeological Museum of L’Almoina (Valencia, Spain)

Ramírez

Zarzo

García-Diego

2021

Sensors

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An earlier study carried out in 2010 at the archaeological site of L’Almoina (Valencia, Spain) found marked daily fluctuations of temperature, especially in summer. Such pronounced gradient is due to the design of the museum, which includes a skylight as a ceiling, covering part of the remains in the museum. In this study, it was found that the thermal conditions are not homogeneous and vary at different points of the museum and along the year. According to the European Standard EN10829, it is necessary to define a plan for long-term monitoring, elaboration and study of the microclimatic data, in order to preserve the artifacts. With the aforementioned goal of extending the study and offering a tool to monitor the microclimate, a new statistical methodology is proposed. For this propose, during one year (October 2019–October 2020), a set of 27 data-loggers was installed, aimed at recording the temperature inside the museum. By applying principal component analysis and k-means, three different microclimates were established. In order to characterize the differences among the three zones, two statistical techniques were put forward. Firstly, Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was applied to a set of 671 variables extracted from the time series. The second approach consisted of using a random forest algorithm, based on the same functions and variables employed by the first methodology. Both approaches allowed the identification of the main variables that best explain the differences between zones. According to the results, it is possible to establish a representative subset of sensors recommended for the long-term monitoring of temperatures at the museum. The statistical approach proposed here is very effective for discriminant time series analysis and for explaining the differences in microclimate when a net of sensors is installed in historical buildings or museums.

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“…Scientists cannot but notice the loss of balance in economic development, the turbulence of emerging trends, and the variability of basic conditions and preconditions. The authors relied on the basic concepts of mathematical modeling for the digital economy proposed in the works of contemporary economists (Galbraith and Hodgson 2018;De Gaetano 2018;Yakovlev et al 2019). It is the use of the mathematical apparatus within the framework of evaluating the economic phenomena that contributes to the most effective analysis of their essential characteristics and provisions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Innovative activities require significant financial resources with simultaneously high investment risk. While the issues of R&D investment at the level of an individual enterprise (Ko et al 2018;Maydanova and Ilin 2019) or state (Agumbayeva et al 2019;Vukovic et al 2017) are now sufficiently studied, at the macro level, taking into consideration synergetic, combinatorial effects of different innovations, the methodology is poorly developed (Bataev 2018;De Gaetano 2018).…”

Section: Obtaining and Collecting Datamentioning

confidence: 99%

Building a Model for Financial Management of Digital Technologies in the Areas of Combinatorial Effects

et al. 2021

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The race of states for digital leadership faces the challenges of digital transformation foresight, providing combinatorial effects and acceleration of digitalization processes, contributing to a more efficient allocation of limited financial resources. This situation determines the relevance of the authors’ study, which aims to develop an appropriate model for the allocation of funds for digital technologies. The methodology of the study is based on the concepts of technological modes and cyclical development of the economy. The paper presents the authors’ definition of the concept “digital space,” which is new to Russian scientific literature. The key research method is mathematical modeling. The scientific novelty of the paper is chronological architectonics of digital transformations, built by the authors, and the model of distribution of financing under the investment constraints due to the trust limit of the society institutions, the time of fluctuations of the system of technological adaptation, and the response of the institutional environment to the digital limits of the transformations. The practical significance of the proposed models is the identified tandem interconnected digital technologies of the areas of combinatorial effects that accelerate the dynamics of Industry 4.0, as well as the resulting tools for the effective management of digital technology financing. The obtained theoretical and empirical results are desirable for the heads of government agencies, foundations, corporations, and development agencies, as well as enterprises interested in accelerating digital transformation and increasing strategic competitiveness in the digital space.

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Forecast Combinations in the Presence of Structural Breaks: Evidence from U.S. Equity Markets

Cited by 8 publications

References 46 publications

A Methodology for Discriminant Time Series Analysis Applied to Microclimate Monitoring of Fresco Paintings

A Methodology for Discriminant Time Series Analysis Applied to Microclimate Monitoring of Fresco Paintings

Multivariate Time Series Analysis of Temperatures in the Archaeological Museum of L’Almoina (Valencia, Spain)

Building a Model for Financial Management of Digital Technologies in the Areas of Combinatorial Effects

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