Study of COVID19 Impact on Moroccan Financial Market Based on ARDL

The aim of this work is to study the impact of the COVID-19 pandemic new cases on the Moroccan financial market using the Autoregressive Distributed Lag (ARDL) approach. The analysis focuses on the relationship between the natural logarithm of the Moroccan All Shares Index (MASI) price and the natural logarithm of new daily cases of COVID-19 in the short term as well as in the long term. A cointegration test is performed on the daily time series for the period from March 3, 2020 to February 11, 2022. A causality test of Toda-Yamamoto is also applied on the variables. The implementation of the forecast with the ARDL method improves the forecast accuracy by 8% to achieve 26.7%. The implementation of the forecast with the ARDL method shows that the addition of the lag of COVID19, the trend and the seasonality makes it possible to achieve a MAPE of 26.7% by improving it by 8% compared to the forecast with the lag of the price only. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

New Statistical Approaches for Modeling the COVID-19 Data Set: A Case Study in the Medical Sector

Statistical distributions have great applicability for modeling data in almost every applied sector. Among the available classical distributions, the inverse Weibull distribution has received considerable attention. In the practice of distribution theory, numerous methods have been studied and suggested/introduced to increase the flexibility level of the traditional probability distributions. In this paper, we implement different distribution methods to obtain five new different versions of the inverse Weibull model. The new modifications of the inverse Weibull model are called the logarithm transformed-inverse Weibull, a flexible reduced logarithmic-inverse Weibull, the weighted TX-inverse Weibull, a new generalized-inverse Weibull, and the alpha power transformed extended-inverse Weibull distributions. To illustrate the flexibility and applicability of the new modifications of the inverse Weibull model, a biomedical data set is analyzed. The data set consists of 108 observations and represents the mortality rate of the COVID-19-infected patients. The practical application shows that the new generalized-inverse Weibull is the best modification of the inverse Weibull distribution.

Wagner’s Law vs. Keynesian Hypothesis: Dynamic Impacts

This study analyzes the dynamics between public expenditure and economic growth in Peru for 1980Q1–2021Q4. We used quarterly time series of real GDP, public consumption expenditure, public expenditure, and the share of public expenditure to output. The variables were transformed into natural logarithms, wherein only the logarithm of public expenditure to output ratio is stationary and the others are non-stationary I1. The study of stationary time series assesses whether Wagner’s law, the Keynesian hypothesis, the feedback hypothesis, or the neutrality hypothesis is valid for the Peruvian case according to Granger causality. We found cointegration between real GDP and public expenditure, and public consumption expenditure and real GDP. Estimating error correction and autoregressive distributed lag models, we concluded that Wagner’s law and the Keynesian hypothesis are valid in the Peruvian case, expressed as dynamic processes that allow us to obtain short-run and long-run impacts, permitting the mutual sustainability of economic growth and public expenditure.

Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review

Quantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement in machine learning algorithms. Furthermore, the processing of classical data and machine learning algorithms in the quantum domain has given rise to an emerging field like quantum machine learning. Recently, quantum machine learning has become quite a challenging field in the case of healthcare applications. As a result, quantum machine learning has become a common and effective technique for data processing and classification across a wide range of domains. Consequently, quantum machine learning is the most commonly used application of quantum computing. The main objective of this work is to present a brief overview of current state-of-the-art published articles between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature review techniques such as research questions and quality metrics of the articles. Initially, we discovered 3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled 30 articles that comply with the quantum machine learning models and quantum circuits using biomedical data. Eventually, this article provides a broad overview of quantum machine learning limitations and future prospects.