Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (5)

Search Parameters:
Keywords = Bayesian structural time series (BSTS)

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
26 pages, 4918 KiB  
Article
Is Bitcoin a Safe-Haven Asset During U.S. Presidential Transitions? A Time-Varying Analysis of Asset Correlations
by Pathairat Pastpipatkul and Htwe Ko
Int. J. Financial Stud. 2025, 13(3), 134; https://doi.org/10.3390/ijfs13030134 - 22 Jul 2025
Viewed by 604
Abstract
Amid the growing debate over how cryptocurrencies are reshaping global finance, this study explores the nexus between Bitcoin, Brent Crude Oil, Gold and the U.S. Dollar Index. We used a time-varying vector autoregressive (tvVAR) model to examine the connection among these four assets [...] Read more.
Amid the growing debate over how cryptocurrencies are reshaping global finance, this study explores the nexus between Bitcoin, Brent Crude Oil, Gold and the U.S. Dollar Index. We used a time-varying vector autoregressive (tvVAR) model to examine the connection among these four assets during the Trump (2017–2020) and Biden (2021–2024) governments. The 48-week return forecast of the Bitcoin–Gold correlation was also conducted by using the Bayesian Structural Time Series (BSTS) model. Results indicate that Bitcoin was the most volatile asset, while the U.S. Dollar remained the least volatile under both regimes. Under Trump, U.S. Dollar significantly influenced Oil and Bitcoin while Bitcoin and Gold were negatively linked to Oil and positively associated with U.S. Dollar. An inverse relationship between Bitcoin and Gold also emerged. Under Biden, Bitcoin, Gold, and U.S. Dollar all significantly affected Oil with Bitcoin showing a positive impact. Bitcoin and Gold remained negatively correlated though not significantly, and the Dollar maintained positive ties with both. Forecasts show a positive link between Bitcoin and Gold in the coming year. However, Bitcoin does not exhibit consistent characteristics of a safe-haven asset during the U.S. presidential transitions examined, largely due to its high volatility and unstable correlations with a traditional safe-haven asset, Gold. This study contributes to the understanding of shifting relationships between digital and traditional assets across political regimes. Full article
Show Figures

Figure 1

40 pages, 28745 KiB  
Article
Bayesian Structural Time Series and Geographically Weighted Logistic Regression Modelling Impacts of COVID-19 Lockdowns on the Spatiotemporal Patterns of London’s Crimes
by Rui Wang and Yijing Li
ISPRS Int. J. Geo-Inf. 2024, 13(1), 18; https://doi.org/10.3390/ijgi13010018 - 4 Jan 2024
Cited by 2 | Viewed by 4492
Abstract
Given the paramount impacts of COVID-19 on people’s lives in the capital of the UK, London, it was foreseeable that the city’s crime patterns would have undergone significant transformations, especially during lockdown periods. This study aims to testify the crime patterns’ changes in [...] Read more.
Given the paramount impacts of COVID-19 on people’s lives in the capital of the UK, London, it was foreseeable that the city’s crime patterns would have undergone significant transformations, especially during lockdown periods. This study aims to testify the crime patterns’ changes in London, using data from March 2020 to March 2021 to explore the driving forces for such changes, and hence propose data-driven insights for policy makers and practitioners on London’s crime deduction and prevention potentiality in post-pandemic era. (1) Upon exploratory data analyses on the overall crime change patterns, an innovative BSTS model has been proposed by integrating restriction-level time series into the Bayesian structural time series (BSTS) model. This novel method allows the research to evaluate the varied effects of London’s three lockdown periods on local crimes among the regions of London. (2) Based on the predictive results from the BSTS modelling, three regression models were deployed to identify the driving forces for respective types of crime experiencing significant increases during lockdown periods. (3) The findings solidified research hypotheses on the distinct factors influencing London’s specific types of crime by period and by region. In light of the received evidence, insights on a modified policing allocation model and supporting the unemployed group was proposed in the aim of effectively mitigating the surges of crimes in London. Full article
Show Figures

Figure 1

16 pages, 2784 KiB  
Article
No Change of Pneumocystis jirovecii Pneumonia after the COVID-19 Pandemic: Multicenter Time-Series Analyses
by Dayeong Kim, Sun Bean Kim, Soyoung Jeon, Subin Kim, Kyoung Hwa Lee, Hye Sun Lee and Sang Hoon Han
J. Fungi 2021, 7(11), 990; https://doi.org/10.3390/jof7110990 - 19 Nov 2021
Cited by 3 | Viewed by 3154
Abstract
Consolidated infection control measures imposed by the government and hospitals during COVID-19 pandemic resulted in a sharp decline of respiratory viruses. Based on the issue of whether Pneumocystis jirovecii could be transmitted by airborne and acquired from the environment, we assessed changes in [...] Read more.
Consolidated infection control measures imposed by the government and hospitals during COVID-19 pandemic resulted in a sharp decline of respiratory viruses. Based on the issue of whether Pneumocystis jirovecii could be transmitted by airborne and acquired from the environment, we assessed changes in P. jirovecii pneumonia (PCP) cases in a hospital setting before and after COVID-19. We retrospectively collected data of PCP-confirmed inpatients aged ≥18 years (N = 2922) in four university-affiliated hospitals between January 2015 and June 2021. The index and intervention dates were defined as the first time of P. jirovecii diagnosis and January 2020, respectively. We predicted PCP cases for post-COVID-19 and obtained the difference (residuals) between forecasted and observed cases using the autoregressive integrated moving average (ARIMA) and the Bayesian structural time-series (BSTS) models. Overall, the average of observed PCP cases per month in each year were 36.1 and 47.3 for pre- and post-COVID-19, respectively. The estimate for residuals in the ARIMA model was not significantly different in the total PCP-confirmed inpatients (7.4%, p = 0.765). The forecasted PCP cases by the BSTS model were not significantly different from the observed cases in the post-COVID-19 (−0.6%, 95% credible interval; −9.6~9.1%, p = 0.450). The unprecedented strict non-pharmacological interventions did not affect PCP cases. Full article
(This article belongs to the Special Issue Advances in Pneumocystis Infection)
Show Figures

Figure 1

17 pages, 1744 KiB  
Article
The Effect of Market-Oriented Government Fiscal Expenditure on the Evolution of Industrial Structure: Evidence from Shenzhen, China
by Yumin Shu and Zhongying Qi
Sustainability 2020, 12(9), 3703; https://doi.org/10.3390/su12093703 - 3 May 2020
Cited by 5 | Viewed by 2939
Abstract
For looking at the effect of public fiscal expenditure of local government on industry, three contradictory points of view: improving effect, impeding effect, and no effect, have been previously discussed in the literature. However, there is no general agreement yet. As the most [...] Read more.
For looking at the effect of public fiscal expenditure of local government on industry, three contradictory points of view: improving effect, impeding effect, and no effect, have been previously discussed in the literature. However, there is no general agreement yet. As the most mature region of China’s socialist market economy with Chinese characteristics, the effect of Shenzhen’s market-oriented fiscal expenditure on the evolution of its industrial structure is worth investigating. This study applies Shenzhen’s fiscal expenditure data and industrial value-added data from 1980 to 2017 to a Bayesian Structure Time Series Model (BSTS). Empirical results show that in Shenzhen, market-oriented public fiscal expenditure presents a significant effect on the evolution of industrial structure. In addition, the promotion effect of different types of public fiscal expenditure on secondary industry is significant but largely subsides later. However, the promotion effect on tertiary industry is comparatively stable. This study suggests that Shenzhen government should apply different types of public fiscal expenditure at least five years in advance to promote the growth of secondary industry and apply fiscal expenditure to promote the tertiary industry when needed. Full article
(This article belongs to the Section Economic and Business Aspects of Sustainability)
Show Figures

Figure 1

12 pages, 1439 KiB  
Article
Bayesian Structural Time Series and Regression Modeling for Sustainable Technology Management
by Sunghae Jun
Sustainability 2019, 11(18), 4945; https://doi.org/10.3390/su11184945 - 10 Sep 2019
Cited by 19 | Viewed by 4568
Abstract
Many companies take the sustainability of their technologies very seriously, because companies with sustainable technologies are better able to survive in the market. Thus, sustainable technology analysis is important issue in management of technology (MOT). In this paper, we study the management of [...] Read more.
Many companies take the sustainability of their technologies very seriously, because companies with sustainable technologies are better able to survive in the market. Thus, sustainable technology analysis is important issue in management of technology (MOT). In this paper, we study the management of sustainable technology (MOST). This focuses on the sustainable technology in various MOT fields. In the MOST, sustainable technology analysis is dependent on time periods. We propose a method of sustainable technology analysis using a Bayesian structural time series (BSTS) model based on time series data. In addition, we use the Bayesian regression to find the relational structure between technologies. To show the performance of our method and how the method can be applied to practical works, we carry out a case study using the patent data related to artificial intelligence technologies. Full article
(This article belongs to the Section Economic and Business Aspects of Sustainability)
Show Figures

Figure 1

Back to TopTop