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'...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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)....

In this paper, we explore how asset returns used as a proxy to detect interconnectedness of systemic risk in the financial system. Our sample employs a mixture of Indonesian banks' public and prudential data over the 2012-2019 period. Using the Principal Component Analysis and Granger causality...

An outlier is a datum that is far from other data points in which it occurs. It can have a considerable impact on the output. Therefore, removing or resolving it before the analysis is essential to prevent skewing. Outliers in a survey sampling can have a significant outcome on statistical...

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...

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...

The purpose of this research is to investigate the factors associated with internship programs and the relationships between internship experiences in tourism and hospitality industry and aspect of future career in the same industry. A principal component analysis was used to determine the...

The dynamic development of the digitized society generates large-scale information data flows. Therefore, data need to be compressed in a way allowing its content to remain complete and informative. In order for the above to be achieved, it is advisable to use the principal component method...

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,...

Daten zu kulturellen Einrichtungen in Deutschland sind von großer Heterogenität hinsichtlich Verfügbarkeit und analytischer Vergleichbarkeit geprägt. Dies wird in Bemühungen um eine raumplanerisch motivierte Typisierung von Städten deutlich, die sich bislang insbesondere auf Großstädte...

The importance of the components of a system has been widely studied in the literature. The main objective of such component importance measures (CIMs) is to evaluate the effect that the components can cause in the system under study by not being present or to quantify the participation of their...

This paper aims to construct a bi-directional, financial liberalization index for Pakistan by considering various financial policy indicators (reforms). This study, by employing the principal component analysis method over a period of 1980-2018 (39 years), aims to determine the composite outcome...