Econometrics最新文献_第5页

Cluster Analysis and Visualisation Describing the Phenomenon of the Covid-19 Virus Pandemic 描述Covid-19病毒大流行现象的聚类分析和可视化

IF 1.5

Econometrics Pub Date : 2023-06-01 DOI: 10.15611/eada.2023.2.03

G. Trzpiot, Zuzanna Krysiak

{"title":"Cluster Analysis and Visualisation Describing the Phenomenon of the Covid-19 Virus Pandemic","authors":"G. Trzpiot, Zuzanna Krysiak","doi":"10.15611/eada.2023.2.03","DOIUrl":"https://doi.org/10.15611/eada.2023.2.03","url":null,"abstract":"Abstract The article refers to the topic of the SARS CoV-2 virus pandemic and focuses on the effect of vaccines against this virus. The relation between the administered vaccines and the development of the global pandemic is very pertinent as the problem is being faced by the whole world. The difficulty lies in the fight against the pandemic, which is the cause of the very high death rate due to the virus, and has caused a global economic crisis. Demonstrating patterns and possible anomalies between data on the number of people vaccinated and the course of the disease and the number of deaths is an important factor in raising awareness of the risk of spreading the virus. The methods presented in the second chapter are data agglomeration and the k-means method. The study compared the results obtained in six selected countries from different regions of the world and presented the most important factors influencing the development of the pandemic. The presented methodology was also the basis for a deeper discussion of the factors determining the spread of the virus and can be an introduction to the analysis of time series. At the same time, it enabled the creation of patterns related to the studied phenomenon (for selected countries) defining local factors contributing to the spread of the disease and determining the effectiveness of the vaccines administered in them. The empirical analysis was conducted on the basis of data available in the electronic scientific publication https://ourworldindata.org/. The visualisations were made in the Tableau program, and the cluster analysis was carried out using the Statistica package.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":"27 1","pages":"45 - 61"},"PeriodicalIF":1.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41889571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digitalization and the Information Society in Algeria: Digital Transformation Actors and Key Variables 阿尔及利亚的数字化和信息社会:数字化转型行动者和关键变量

IF 1.5

Econometrics Pub Date : 2023-06-01 DOI: 10.15611/eada.2023.2.02

Maria Bouberka, S. Fadel, Hassan Derrar

{"title":"Digitalization and the Information Society in Algeria: Digital Transformation Actors and Key Variables","authors":"Maria Bouberka, S. Fadel, Hassan Derrar","doi":"10.15611/eada.2023.2.02","DOIUrl":"https://doi.org/10.15611/eada.2023.2.02","url":null,"abstract":"Abstract The information society is a product of the intersection of socio-historical-technological contexts, where the development of science and industry has a global reach. The pervasiveness of new information and communication technologies makes it an essential component of a new civilization, affecting every country in varying degrees. Although this techniques approach is grounded in some truth, it neglects other vital aspects that challenge the idea of an information society that prioritizes human needs. The leading nations have achieved this through active research and development, driven by government involvement and the significant contributions of universities. Furthermore, the collaboration of diverse economic, institutional, social, and civic actors has played an essential role in its advancement. However, constructing and promoting an information society transcends infrastructure. It involves political actions that consider the socio-technological nature of this development and its impact on society and other sectors of activity. The objective of this article is twofold. On the one hand, the study analysed the situation of Algeria and its position in relation to other countries regarding the information society, and the other examined the factors that influence the development of the information society in Algeria, trying to identify the most important ones. Finally, the authors proposed a development strategy in this area. The research thesis was formulated as follows: What are the key variables that have an impact on the development of digitalization and the information society in Algeria? Thus, the regulatory frameworks of the most advanced countries are central to the initiatives aimed at its development. To foster the emergence of an information society in Algeria, solidarity must be strengthened, diversity promoted, and the potential of all citizens catalysed.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":"27 1","pages":"21 - 44"},"PeriodicalIF":1.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41377297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online Hybrid Neural Network for Stock Price Prediction: A Case Study of High-Frequency Stock Trading in the Chinese Market 用于股票价格预测的在线混合神经网络——以中国市场高频股票交易为例

IF 1.5

Econometrics Pub Date : 2023-05-18 DOI: 10.3390/econometrics11020013

Chengyu Li, Luyi W. Shen, G. Qian

{"title":"Online Hybrid Neural Network for Stock Price Prediction: A Case Study of High-Frequency Stock Trading in the Chinese Market","authors":"Chengyu Li, Luyi W. Shen, G. Qian","doi":"10.3390/econometrics11020013","DOIUrl":"https://doi.org/10.3390/econometrics11020013","url":null,"abstract":"Time-series data, which exhibit a low signal-to-noise ratio, non-stationarity, and non-linearity, are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in huge quantities. For this purpose, it is essential to apply a trading method that is capable of fast and accurate prediction from such time-series data. In this paper, we developed an online time series forecasting method for high-frequency trading (HFT) by integrating three neural network deep learning models, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), and transformer; and we abbreviate the new method to online LGT or O-LGT. The key innovation underlying our method is its efficient storage management, which enables super-fast computing. Specifically, when computing the forecast for the immediate future, we only use the output calculated from the previous trading data (rather than the previous trading data themselves) together with the current trading data. Thus, the computing only involves updating the current data into the process. We evaluated the performance of O-LGT by analyzing high-frequency limit order book (LOB) data from the Chinese market. It shows that, in most cases, our model achieves a similar speed with a much higher accuracy than the conventional fast supervised learning models for HFT. However, with a slight sacrifice in accuracy, O-LGT is approximately 12 to 64 times faster than the existing high-accuracy neural network models for LOB data from the Chinese market.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48511547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Information-Criterion-Based Lag Length Selection in Vector Autoregressive Approximations for I(2) Processes 基于信息准则的I(2)过程向量自回归逼近滞后长度选择

IF 1.5

Econometrics Pub Date : 2023-04-20 DOI: 10.3390/econometrics11020011

D. Bauer

引用次数: 0

Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models 基于状态切换未观察成分模型的COVID-19感染率建模

IF 1.5

Econometrics Pub Date : 2023-04-03 DOI: 10.3390/econometrics11020010

Paul Haimerl, Tobias Hartl

引用次数: 0

Detecting Common Bubbles in Multivariate Mixed Causal–Noncausal Models 多元因果-非因果混合模型中常见气泡的检测

Econometrics Pub Date : 2023-03-09 DOI: 10.3390/econometrics11010009

Gianluca Cubadda, Alain Hecq, Elisa Voisin

引用次数: 0

The Application of Association Rules to Detect the Effects of Vaccinations against Covid-19 in the EU-27. Preliminary Estimates 关联规则在欧盟27国检测Covid-19疫苗接种效果中的应用初步估计

IF 1.5

Econometrics Pub Date : 2023-03-01 DOI: 10.15611/eada.2023.1.01

K. Berezka, Olha Kovalchuk

{"title":"The Application of Association Rules to Detect the Effects of Vaccinations against Covid-19 in the EU-27. Preliminary Estimates","authors":"K. Berezka, Olha Kovalchuk","doi":"10.15611/eada.2023.1.01","DOIUrl":"https://doi.org/10.15611/eada.2023.1.01","url":null,"abstract":"Abstract In this research study, the authors obtained the preliminary evaluation of the impact detection of vaccinations against COVID-19 in the EU-27. The empirical basis of the study was the daily number of COVID-19 cases, vaccinations, hospitalisations, and deaths in the EU countries from March 2020 to March 2022. Rules of association were used to identify non-obvious associations between vaccinations against COVID-19 and cases of illness, hospitalisations, and deaths from COVID-19. The obtained results were used to cluster the EU countries by the level of vaccinations against COVID-19, cases of COVID-19, deaths from COVID, and COVID-19 hospitalisations for the EU member states. The K-means clustering method was used for cluster analysis. Hidden dependencies of the number of COVID-19 cases, the number of COVID-19 hospitalisations, and the number of COVID-19 deaths due to the number of vaccinations against COVID-19 by EU countries were revealed. It was established with a high probability that vaccination significantly affects the level of morbidity. For the first time, association rules were obtained, which are preliminary estimates of the relationship between the dynamics of vaccinations against COVID-19 and the dynamics of COVID-19 cases, COVID-19 hospitalisations, and deaths from COVID-19 in the EU. The results can be used to make beneficial decisions, for example, to regulate vaccination policies in individual EU countries, and predict the future consequences of the COVID-19 pandemic.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":"27 1","pages":"1 - 16"},"PeriodicalIF":1.5,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43373985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

The Prospect Theory and First Price Auctions: an Explanation of Overbidding 前景理论与首价拍卖:对超竞价的解释

IF 1.5

Econometrics Pub Date : 2023-03-01 DOI: 10.15611/eada.2023.1.03

Dushko Josheski, M. Apostolov

引用次数: 0

The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random 函数上标量回归结构中模态函数的条件密度估计&随机缺失的局部线性方法

IF 1.5

Econometrics Pub Date : 2023-03-01 DOI: 10.15611/eada.2023.1.02

Wahiba Bouabsa

引用次数: 0

Factorization of a Spectral Density with Smooth Eigenvalues of a Multidimensional Stationary Time Series 多维平稳时间序列具有光滑特征值的谱密度的因子分解

IF 1.5

Econometrics Pub Date : 2023-02-26 DOI: 10.3390/econometrics11020014

T. Szabados

引用次数: 0