This paper presents a distribution-free test, based on the permutation approach, on treatment effects with a multivariate categorical response variable. The motivating example is a typical case-control biomedical study, performed to investigate the effect of the treatment called "assisted motor...

This paper develops new identification results for multidimensional continuous measurement-error models where all observed measurements are contaminated by potentially correlated errors and none provides an injective mapping of the latent distribution. Using third-order cross-moments, the paper...

Examining the persistence of volatility transmission over an extended timeframe, regardless of specific events, reveals significant importance, as it uncovers the inherent fundamental and structural drivers that give rise to volatility. However, previous research in South Asia is minimal and has...

This paper compares the statistical and economic performance of state-of-the-art highfrequency based multivariate volatility models with a simpler, widely used alternative-the Exponentially Weighted Moving Average (EWMA) filter. Using over two decades of 100 U.S. stock returns (2002-2023), we...

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...

This paper introduces a novel multivariate composite estimator for the Labour Force Survey (LFS). Unlike the univariate composite estimators used in some countries, the multivariate estimator takes into account the different probabilities of transitioning between labour market categories, such...

In the analysis of multivariate stochastic volatility models, many estimation procedures begin by transforming the data, taking the logarithm of the squared returns to obtain a linear state space model. A well-known series representation links the correlations between elements of the observation...

This paper presents a novel copula-based autoregressive framework for multilayer arrays of integer-valued time series with tensor structure. It complements recent advances in tensor time series that predominantly focus on real-valued data and overlook the unique properties of integer-valued time...

In the last few decades, the study of ordinal data in which the variable of interest is not exactly observed but only known to be in a specific ordinal category has become important. In Psychometrics such variables are analysed under the heading of item response models (IRM). In Econometrics,...

The article presents a proposal for a goodness-of-fit test for multivariate normality. The idea of the test is based on the empty cells test, which is well known in the literature. In the empty cells test, the area of the random variable's variability is divided into m disjoint cells. Assuming...

The purpose of the study is to determine the impact of demographic changes (depopulation, population aging) occurring in Polish local government units (LGUs), identified in this article with gminas (municipalities), on their revenue potential. The authors analyzed the sources of the own revenue...

This paper introduces two methodological improvements to the Hodrick- Prescott (HP) filter for decomposing GDP into trend and cycle components. First, we propose a robust univariate filter that accounts for extreme observations - such as the COVID-19 pandemic - by treating them as additive...

This study aims to deepen our understanding of the evolving landscape of public procurement (PP) and emerging technologies (ET). It addresses the critical gap in scholarly production analysis, author collaborations, and thematic trends in PP and ET, providing valuable insights for researchers,...

Purpose The main purpose of the paper is to identify the most important directions of research to date and to indicate new, emerging areas of research concerned with application of financial technology (FinTech) solutions in microfinance companies. Design/methodology/approach This paper...

In the last few decades, the study of ordinal data in which the variable of interest is not exactly observed but only known to be in a specific ordinal category has become important. In Psychometrics such variables are analysed under the heading of item response models (IRM). In Econometrics,...

This paper introduces an extended multivariate EGARCH model that overcomes the zero-return problem and allows for negative news and volatility spillover effects, making it an attractive tool for multivariate volatility modeling. Despite limitations, such as noninvertibility and unclear...

Accurate forecasting of solar PV generation is critical for integrating renewable energy into power systems. This paper presents a multivariate probabilistic forecasting model that addresses the challenges posed by imbalanced data resulting from day and night-time periods in solar photovoltaic...

This paper shows that the integrated modified OLS (IM-OLS) estimator developed for cointegrating linear regressions in Vogelsang and Wagner (2014a) can be straightforwardly extended to cointegrating multivariate polynomial regressions. These are regression models that include as explanatory...

This paper presents a credit gap for Malta derived from a semi-structural multivariate filter. This modelling approach has several advantages over univariate approaches typically used, for example to construct the Basel gap. The multivariate filtering of observed data into trends and cycles is...

This study introduces a multivariate extension to the class of stochastic volatility models, employing integrated nested Laplace approximations (INLA) for estimation. Bayesian methods for estimating stochastic volatility models through Markov Chain Monte Carlo (MCMC) can become computationally...

This study aims to deepen our understanding of the evolving landscape of public procurement (PP) and emerging technologies (ET). It addresses the critical gap in scholarly production analysis, author collaborations, and thematic trends in PP and ET, providing valuable insights for researchers,...

The complexity of estimating multivariate GARCH models increases significantly with the increase in the number of asset series. To address this issue, we propose a general regularization framework for high-dimensional GARCH models with BEKK representations, and obtain a penalized quasi-maximum...