This study develops a multidimensional classification of Latin American and Caribbean countries based on a multidimensional set of economic, social, technological, and environmental indicators. This study develops a multidimensional assessment of the performance of Latin American and Caribbean...

For decades, there has been consensus that measures of well-being must move past readily available economic data, and newer measurements of well-being are often obtained through individual questions or questionnaires. Despite being more accessible to compare person to person, recent trends have...

This study develops a multi-dimensional composite index to measure financial inclusion in Ethiopia, using a two-stage Principal Component Analysis based on ten traditional and five digital indicators from 2015 to 2023. The results reveal a significant increase in Ethiopia's financial inclusion...

We consider the applicability of simple statistical models to Russia's short-term economic trends in a wartime context. We develop several composite indicators combining economic variables to predict Russian GDP trends both before and after the invasion if Ukraine in 2022. In addition, our SVAR...

This study conducts a comprehensive analysis of the effects of changes in financial conditions on the economy of Türkiye, for which financial conditions indices were constructed. To examine possible changes in the relationship between financial conditions and the real economy due to the unique...

We propose a new rank-based test for the number of common primitive shocks, q, in large panel data. After estimating a VAR(1) model on r static factors extracted by principal component analysis, we estimate the number of common primitive shocks by testing the rank of the VAR residuals'...

The estimation of age-to-age development factors is fundamental to loss reserving, with direct implications for risk management and regulatory compliance in the auto insurance sector. The precise and robust estimation of these factors underpins the credibility of case reserves and the effective...

This study examines how effectively common factors, extracted using both the partial least squares method and principal component analysis from the business tendency survey and the banking loan tendency survey, can predict Turkiye's economic growth. The findings indicate that integrating this...

Investor sentiment is the result of irrational speculations about the future asset values driven by the market participants. Though scholarly works on investor sentiment are evolving in both developed and emerging markets, the literature in the Indian context is relatively modest. To fill this...

Researchers have been working on developing new effective, reliable, and environmentally friendly crop recommendation systems. This study introduces a thorough framework for predicting crop recommendations by combining sophisticated machine learning (ML) classifiers with a multi-class...

Although the number of econometric analyses related to the renewable energy sector in Azerbaijan is increasing, studies on nonparametric dimensionality reduction are rather sparse. Principal component analysis (PCA) and multiple correspondence analysis (MCA) were chosen to fill this apparent...

Motion artifacts reduce the quality of information in the electroencephalogram (EEG) signals. In this study, we have developed an effective approach to mitigate the motion artifacts in EEG signals by using empirical wavelet transform (EWT) technique. Firstly, we decompose EEG signals into...

The Internet of things (IoT) is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring, surveillance, and healthcare. To address the limitations imposed by inadequate resources, energy, and network scalability, this type...

The disruption of supply chain due to Covid-19 and the war in Ukraine, render the prediction of agricultural output a determinant factor of economic life. We consider the predictability of agricultural output based on a set of explanatory variables, that include agricultural input, prices and...

This study examines the efficacy of machine learning (ML) and artificial intelligence (AI) in predicting profitability, offering valuable insights for investors and managerial decisions. Leveraging the extensive array of data associated with Big Data, ML and AI are compared with regression...

In this paper we propose an efficient and easy to implement model to project each of the Principal Components (PCs) -driving a given portfolio or derivative product, or trading strategy's risk factors- on a number of inputted variables (risk factors) equal to the PC's rank, ranking in terms of...

We have constructed a financial conditions index for Norway (FCIN). The FCIN offers a daily update on Norwegian financial conditions based on data from January 2003 on bank lending rates, bond spreads, the foreign exchange market, the stock market and the housing market. The index is constructed...

Gradient capital allocation, also known as Euler allocation, is a technique used to redistribute diversified capital requirements among different segments of a portfolio. The method is commonly employed to identify dominant risks, assessing the risk-adjusted profitability of segments, and...

In this paper, we develop new methods for analyzing high-dimensional tensor datasets. A tensor factor model describes a high-dimensional dataset as a sum of a low-rank component and an idiosyncratic noise, generalizing traditional factor models for panel data. We propose an estimation algorithm,...

This paper shows that the economy can be described by two factors: an inflationary demand factor and a deflationary supply factor. More specifically, our approach combines the use of several filtering techniques to extract business cycle fluctuations and dynamic principal components analysis...

According to portfolio theory, diversifying investment to several stocks with negative correlations may reduce portfolio risk. In contrast, combining stocks with similar movement (co-movement) has no impact on portfolio risk reduction. This study aims to examine state-owned enterprise stock...

The concept of centrality is widely used to monitor systems with a network structure because it allows identifying their most influential participants. This monitoring task can be difficult if the number of system participants is considerably large or if the wide variety of centrality measures...

This research paper introduces a methodology to assess the robustness of the Global Innovation Index (GII), by comparing the rankings provided in it with those achieved using alternative data-driven methodologies such as data envelopment analysis (DEA) and principal component analysis (PCA)....

When designing a statistical model, applied researchers strive to make the model consistent, unbiased, and efficient. Labor productivity is an important economic indicator that is closely linked to economic growth, competitiveness, and living standards within an economy. This paper proposes the...