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...

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...

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,...

In recent years, the transnational and regional dimensions of tourism have strengthened. Unlike in the past, we can now consider regional tourism policies, such as innovation policies. The recent creation of the UN Tourism Office for the Americas is a step in this direction. Within this...

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...

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...

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...

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 paper extends the multivariate filter approach for estimating potential output and NAIRU developed for Türkiye by integrating the capacity utilization block into the model. The model gives more negative estimates of the output gap and smaller estimates of NAIRU in recession periods...

Multivariate random sums appear in many scientific fields, most notably in actuarial science, where they model both the number of claims and their sizes. Unfortunately, they pose severe inferential problems. For example, their density function is analytically intractable, in the general case,...

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...

This paper disentangles the added value of using high-frequency-based (realized) covariance measures on multivariate volatility forecasting into two pillars: the realized variances and realized correlations and quantifies the corresponding economic gains using a broad set of portfolio...

Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating...

This research proposes a novel arbitrage approach in multivariate pair trading, termed the Optimal Trading Technique (OTT). We present a method for selectively forming a "bucket" of fiat currencies anchored to cryptocurrency for monitoring and exploiting trading opportunities simultaneously. To...

Optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These microstructures are defined by the material...

We propose an asymptotic N(0, 1) inferential strategy to test for volatility spillover between markets consisting of multiple sectors. First, we use nonparametric kernel method to derive test statistics that assign flexible weight to each lag order and are able to check a growing number of lags...

For risk management, implementing risk measures and backtesting them are essential tasks. Since the expected shortfall (ES) possesses coherence and tail sensitivity, the Basel Committee has raised the option to replace the classical risk measure value-at-risk (VaR) with ES. However, the...

Portfolio selection is often faced with large noisy data sets of strongly correlated asset returns, and so is prone to unstable portfolio weights and serious estimation error. To attenuate these problems, this paper proposes a new latent factor model equipped with both a suitable robust...

This article proposes a framework for determining credibility premiums for multiple coverages in a compound risk model with Tweedie distribution. The framework builds upon previous results on credibility premium and provides an explicit multivariate credibility premium formula that is applicable...

Disaggregation of mortality by cause has advanced the development of life tables for life insurance and pension purposes. However, the assumption that the causes of death are independent is a challenge in reality. Furthermore, models that determine relationships among causes of death such as...

Over the span of a decade, the spectrum of online activities has expanded worldwide. From casual browsing to social networking, from education to entertainment, and from engaging in commercial to philanthropic pursuits, the array of online activities has continued to diversify in scope. Some...

A recently introduced approach is extended to probabilistic electricity price forecasting (EPF) utilizing distributional artificial neural networks, based on a regularized distributional multilayer perceptron (DMLP). We develop this technique for a multivariate case EPF with incorporated...

We provide a simple identity that decomposes a product moment of multivariate normal random variables as a sum of various products of univariate moments of one of the random variables and multivariate moments of the other random variables. The new identity allows for much faster computation of...

With the impacts of climate change intensifying, sea level is expected to rise 0.25–0.30 meters by 2050. With a one-degree Celsius rise in temperature, precipitation globally is expected to increase by 7%. An important service linked to these pressures are roads, which are known to flood and...

This article offers a new paradigm for analyzing the behavior of uncertain multivariable systems using a set of quantities we call inferential moments. Marginalization is an uncertainty quantification process that averages conditional probabilities to quantify the expected value of a probability...