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The purpose of this study was to identify and measure the impact of the different effects of entropy states over the high-frequency trade of the cryptocurrency market, especially in Bitcoin, using and selecting optimal parameters of the Bayesian approach, specifically through approximate Bayesian computation (ABC). ABC corresponds to a class of computational methods rooted in Bayesian statistics that could be used to estimate the posterior distributions of model parameters. For this research, ABC was applied to estimate the daily prices of the Bitcoin cryptocurrency from May 2013 to December 2021. The findings suggest that the behaviour of the parameters for our tested trading algorithms, in which sudden jumps are observed, can be interpreted as changes in states of the generated time series. Additionally, it is possible to identify and model the effects of the COVID-19 pandemic on the series analysed in the research. Finally, the main contribution of this research is that we have characterised the relationship between entropy and the evolution of parameters defining the optimal selection of trading algorithms in the financial industry. [ FROM AUTHOR] Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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This paper studies the change in the distance traveled by domestic tourists considering the pre- and post-pandemic outbreak summer periods of 2019 and 2020. Using representative monthly microdata involving more than 31,000 trips conducted by Spanish residents, we examine the heterogeneity in behavioral adaptation to COVID-19 based on sociodemographic and trip-related characteristics. To account for selection effects and the potential change in the population composition of travelers between the two periods, we estimate an endogenous switching regression that conducts separate regressions for the pre- and post-pandemic periods in a unified econometric framework. Our results point to heterogeneous shifts in the distance traveled by domestic travelers after COVID-19 outbreak per sociodemographic group, with notable differences by travel purpose and lower relevance of traditional determinants like income.
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For a city to become resilient, smart measures must be taken that can cope with unexpected events such as the arrival of a pandemic or natural disasters caused by climate change and also prevent further destruction of our planet. Transport powered by combustion engines is one of the main emitters of greenhouse gases that increase the temperature of the planet and cause climate change. The public transport service of the city of Quito is the most used by its inhabitants and is mostly made up of combustion engine buses, however, during certain months of the years 2020 and 2021 it was suspended and the capacity was also limited to prevent the spread of the covid-19 virus, as it has become one of the main sources of contagion. The limitation of public transport led to economic, political and social instability. Some citizens and transport operators chose to break the law due to the limitations of public transport. Given the demand for transport, this study calibrated ten multinomial logit econometric models to estimate the probability of acceptance of the bicycle as an alternative mode of transport to public transport and taxis in the financial sector of Quito, using stated and revealed preference surveys. The bicycle is considered as a sustainable means of transport capable of solving the need to move around with social distance and in situations where public transport is limited. © 2022 IEEE.
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The Covid-19 pandemic has caused numerous variations in the global economies with repercussions in all sectors. Once the emergency phase has finished, the entire worldwide population has changed its lifestyle and has had to adapt to live with the pandemic. In particular, the several modifications that have occurred in the job market and in schools and universities have determined a necessary reorganization of domestic spaces. The present study represents the first phase of a wider research aimed at verifying the transformation in the Italian residential market demand resulted by the Covid-19. The analysis carried out in this work has been performed at the municipal level, by considering the data published by the National Institute of Statistics collected for the 15th General Census of the population and housing in 2011. The dataset collected has been processed through an advanced econometric technique in order to identify the functional relationships between the residential average unit market value and the main architectural, socio-demographic and territorial factors. Further developments of this research will concern the application of the same methodological approach proposed to data detected by the National Institute of Statistics for the 16th Census scheduled for 2021. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The paper identifies the level of importance determined by the consumer as far as traveling and discovering new destinations are concerned, after the lockdown caused by the health crisis triggered by the coronavirus pandemic. A sample of 445 respondents is used, and an ordered logit econometric model is applied to estimate the importance level of travelling and enjoying a new destination considering the respondents' sociodemographic characteristics and the level of importance attributed to meeting new people, visiting their families, going back to work, meeting their friends, eating out in a restaurant and attending musical and other cultural events. The main conclusions are related to consumers who believe it is very important to travel for specific reasons, such as meeting new people and friends or visiting their families, eating out in a restaurant or attending musical or other cultural events. Family and friends play an important role, considering that to visit them is seen as a priority. This study provides a relevant assessment regarding the priorities defined by the consumers in the scope of the discovery of new destinations, thus providing inputs to the different stakeholders involved in the definition of strategies for the promotion of tourism destinations.
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Today, tourism plays an important role in the economic and financial development of countries, and its impact is greater than ever. Therefore, for sustainable economic and financial growth and well-planned development, public and private investments should be directed to areas of priority tourism development. Research on the effect of perceptions on the behavior of tourists in these two countries has not been carried out before, thus, the purpose of this research is to determine whether the effects of the perception of tourists has an impact on economic and financial development based on factors (F1 (f.1.1 and f.1.2) and F2 (f.2.1 and f. 2.2)). For this study, the data were provided by respondents from several cities in Albania and Kosovo. A total of 1002 questionnaires divided into three sessions were analyzed using factor analysis, data reliability analysis, and multiple regression analysis. All analyses were performed using SPSS version 23.0 for Windows. In this case, 23 variables were tested and divided into two factors and five sub-factors. The results showed that special attention should be paid to the following factors: (a) awareness of tourists of facilities in tourist destinations where they would like to visit;(b) greater knowledge of foreign languages for residents of both countries, which could facilitate communication with tourists during purchases or other requests;(c) attracting new investors and the creation by the government bodies of conditions and security for both investors and tourists;(d) supporting the marketing and sale of local products for tourists;(e) the need for infrastructure support from government bodies in both countries in order to increase economic and financial well-being through tourism;(f) the need to implement strategies focusing on the sustainable development of both countries through tourism should be strengthened;(g) in terms of sustainable development and regional competitiveness, Kosovo and Albania should follow development trends and be competitive with other countries in the region. The implications of this paper relate only to certain studied variables, and only in certain cities of Albania and Kosovo. In case of future analysis by different researchers, other variables can be analyzed for different locales by making comparisons with the presented data.
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Telemedicine has been available in Canada for a while but its uptake before the COVID-19 pandemic has been slow. The pandemic has since changed how people in Canada access healthcare by hastening digital transformation in the sector. Pre-pandemic, Canada was behind its international peers in its use of telemedicine. Now, many patient consultations, both primary and specialist, are conducted virtually. RAND Europe researchers examined the potential impact in Canada of continued, long-term use of telemedicine, which can include the use of "smart" devices to conduct medical tests, the digital storage and sharing of medical records, and real-time tele-consultations between healthcare providers and patients. They looked at the quality, access and cost of telemedicine, the barriers that have led to its relatively slow adoption, and what the economic effect would be of an increase in uptake. The study found that, alongside the benefits from tools such as telemonitoring and digital health records, widespread use of teleconsultations could lead to significant benefits for Canadian patients, the Canadian economy, and wider Canadian society. The findings directly contribute to the evidence base in telemedicine and virtual healthcare more generally.
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The COVID-19 spread all around the world, causing more than a million deaths and reaching over 50 million confirmed cases. A forecast of these numbers is vital for the adequate preparations of health care capacities and for the governments to take the necessary decisions. In this study, it is aimed to predict the evolution of COVID-19 figures, employing alternative statistical models such as the Holt-Winters, ARIMA, and ARIMAX while using the time series corresponding to different parameters of this disease such as daily cases, daily deaths, and the stringency index. Considered are the John Hopkins University epidemiological world data and the top ten countries with the highest cases, along with China. The fitting of the time series and the upcoming 10 days projections resulted in a high level of accuracy, presented with alternative error metrics and comparisons between the situations of countries. Holt-Winters is the best performing model, while ARIMAX gives the worst accuracy results. Moreover, it was found that the use of coefficient determination and Bayesian information criterion alone are not suitable, and scale independent metrics should be employed when the data ranges differ. The results of this study would be useful to set up benchmark results for other studies and the projections may be used for medical, economic, and social precaution and preparation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The study examines the role of data frequency and estimation methods in electricity price estimation by applying selected machine learning algorithms and time series econometric models. In this context, Turkey is selected as an emerging country example, seven explanatory variables including COVID-19 pandemic is considered, and daily and weekly data between February 20, 2019 and March 26, 2021 that includes pre-pandemic and pandemic periods are used. The empirical results show that (i) machine learning algorithms perform better than time series econometric models for both pre-pandemic and pandemic periods;(ii) high-frequency data increases the performance of estimation models;(iii) machine learning algorithms perform better with high-frequency (daily) data with regard to low-frequency (weekly) data;(iv) the pandemic causes an adverse effect on the performance of estimation models;(v) energy-related variables are more important than other variables although all are significant;(vi) the share of renewable sources in electricity production is the most important variable on the electricity prices in both periods and data types. Hence, the findings highlight the role of data frequency and method selection in electricity prices estimation. Moreover, policy implications are discussed. © 2022 Elsevier Ltd
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In this research, statistical models are formulated to study the effect of the health crisis arising from COVID-19 in global markets. Breakpoints in the price series of stock indexes are considered. Such indexes are used as an approximation of the stock markets in different countries, taking into account that they are indicative of these markets because of their composition. The main results obtained in this investigation highlight that countries with better institutional and economic conditions are less affected by the pandemic. In addition, the effect of the health index in the models is associated with their non-significant parameters. This is due to that the health index used in the modeling would not determine the different capacities of the countries analyzed to respond efficiently to the pandemic effect. Therefore, the contagion is the preponderant factor when analyzing the structural breakdown that occurred in the world economy.
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This study modelled the reported daily cumulative confirmed, discharged and death Coronavirus disease 2019 (COVID-19) cases using six econometric models in simple, quadratic, cubic and quartic forms and an autoregressive integrated moving average (ARIMA) model. The models were compared employing R-squared and Root Mean Square Error (RMSE). The best model was used to forecast confirmed, discharged and death COVID-19 cases for October 2020 to February 2021. The predicted number of confirmed and death COVID-19 cases are alarming. Good planning and innovative approaches are required to prevent the forecasted alarming infection and death in Ivory Coast. The applications of findings of this study will ensure that the COVID-19 does not crush the Ivory Coast's health, economic, social and political systems.