81 Results Found

Challenging statements have appeared in recent years in the literature on advances in computational procedures.[...]

In the last years, the huge amount of data available in many disciplines makes the mathematical modeling, and, more concretely, econometric models, a very important technique to explain those data. One of the most used of those econometric techniques...

The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses research issues that are related to the various disciplines. Academics could develop...

In the context of accelerated digitization and the transition to sustainability, this study explores the relationship between the use of cloud computing services and greenhouse gas (GHG) emissions in the IT and communications sectors at the European...

This paper develops a dispersion trading strategy based on a statistical index subsetting procedure and applies it to the S&P 500 constituents from January 2000 to December 2017. In particular, our selection process determines appropriate subset...

The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially indu...

This paper develops a fully-fledged statistical arbitrage strategy based on a mean-reverting jump–diffusion model and applies it to high-frequency data of the S&P 500 constituents from January 1998–December 2015. In particular, the es...

This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a co...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to e...

The software for spatial econometrics available in the R system for statistical computing is reviewed. The methods are illustrated in a historical perspective, highlighting the main lines of development and employing historically relevant datasets in...

We provide new analytical results for the implementation of the Hausman specification test statistic in a standard panel data model, comparing the version based on the estimators computed from the untransformed random effects model specification unde...

Even though forecasting methods have advanced in the last few decades, economists still face a simple question: which prediction method gives the most accurate results? Econometric forecasting methods can deal with different types of time series and...

This study provides a systematic overview of 207 econometrics-related R packages identified through CRAN and the Econometrics Task View. Using descriptive and inferential statistics and text mining to compute the word frequency and association among...

The computational revolution in simulation techniques has shown to become a key ingredient in the field of Bayesian econometrics and opened new possibilities to study complex economic and financial phenomena. Applications include risk measurement, fo...

ITISE-2025 (11th International conference on Time Series and Forecasting) seeks to provide a forum for scientists, engineers, educators, and students to discuss the latest ideas and realizations in the foundations, theory, and models of and applicati...

The ITISE 2025 (11th International conference on Time Series and Forecasting) seeks to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applica...

This article introduces the R package pspatreg, which is publicly available for download from the Comprehensive R Archive Network, for estimating semiparametric spatial econometric penalized spline (P-Spline) models. These models can incorporate a no...

The ITISE 2024 (10th International Conference on Time Series and Forecasting) seeks to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in terms of the foundations, theory, models an...

The ITISE 2023 (9th International Conference on Time Series and Forecasting) sought to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applica...

The ITISE 2024 (10th International Conference on Time Series and Forecasting) seeks to provide a discussion forum for scientists, engineers, educators, and students regarding the latest ideas and realizations in the foundations, theory, models, and a...

Real estate valuation increasingly requires methodologies that combine predictive accuracy, economic interpretability, and model transparency. This study proposes a Generalized Explainable Hybrid Econometric–Machine Learning Framework integrati...

Econometric estimation of panel data models by feasible generalized least squares (FGLS) provides an example of how conceptually simple problems may run into computational bottlenecks. I address the main computational tasks of FGLS within the R syste...

This paper introduces advanced computational methods for stock price prediction, integrating Fast Recurrent Neural Networks (FastRNN) with meta-heuristic algorithms such as the Horse Herd Optimization Algorithm (HOA) and the Spotted Hyena Optimizer (...

This paper uses an econometric model and Bayesian estimation to reverse engineer the path of inflation expectations implied by the New Keynesian Phillips Curve and the data. The estimated expectations roughly track the patterns of a number of common...

This study offers a comprehensive assessment of financial market modeling through a PRISMA-based systematic review, bibliometric analysis, and content synthesis. We examined 67 review articles (1990–2024) from Web of Science to build a conceptu...

This study focuses on identifying personality traits in computer science students and determining whether they are related to academic performance. In addition, the importance of the personality traits based on motivation scale and depression, anxiet...

The aim of this paper to give a multidimensional version of the classical one-dimensional case of smooth spectral density. A spectral density with smooth eigenvalues and $H^{\infty }$ eigenvectors gives an explicit method to factorize the spectr...

Plug-in electric vehicles (PEV) are considered to reduce oil dependency, noise, and local air pollution as well as greenhouse gas emissions caused by road transportation. Today, the early market penetration phase has started and can be observed in ma...

Selecting the relevant covariates in high-dimensional panel data remains a central challenge in applied econometrics. Conventional fixed effects and random effects models are not designed for systematic variable selection under model uncertainty. In...

Vehicle congestion and the environmental problems associated with the increasing vehicle fleet have led stakeholders to create solutions to these problems. Park-and-Ride (P&R) facilities are provided as a solution for public transportation to avo...

In recent years, precise economic forecasting has primarily relied on econometric models, which often assume linearity and stationarity in time series data. However, the nonlinear and dynamic nature of economic data calls for more innovative approach...

The SAR model is widely used in spatial econometrics to model Gaussian processes on a discrete spatial lattice, but for large datasets, fitting it becomes computationally prohibitive, and hence, its usefulness can be limited. A computationally-effici...

We propose an end-to-end differentiable framework for hedonic agricultural land pricing that integrates deep learning-based land cover classification with spatial econometric modeling into a single neural architecture. Traditional hedonic pricing app...

In this article, we conduct a statistical analysis of the autocorrelation functions (ACF) of multi-scale logarithmic returns computed over maximal monotonic uninterrupted trends (runs) in financial indices’ daily data. We analyze the Dow Jones...

Debate persists over whether classical econometric or modern machine learning (ML) approaches provide superior forecasts for volatile monthly price series. Despite extensive research, no systematic cross-domain comparison exists to guide model select...

Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alt...

We compare three modern Bayesian approaches, Hamiltonian Monte Carlo (HMC), Variational Bayes (VB), and Integrated Nested Laplace Approximation (INLA), for two classic spatial econometric specifications: the spatial lag model and spatial error model....

Power-law (PL) formalism is known to provide an appropriate framework for canonical modeling of nonlinear systems. We estimated three stochastically distinct models of constant elasticity of substitution (CES) class functions as non-linear inverse pr...

Several disciplines, such as econometrics, neuroscience, and computational psychology, study the dynamic interactions between variables over time. A Bayesian nonparametric model known as the Wishart process has been shown to be effective in this situ...

The main aim of this paper was to propose a new reliability index for steel structure assessment and to check it using the example of a popular Pratt truss girder. Structural analysis was completed in the finite element method system Autodesk ROBOT,...

Volatility break robust panel unit root tests (PURTs) recently proposed by Herwartz and Siedenburg (Computational Statistics & Data Analysis 2008, 53, 137–150) and Demetrescu and Hanck (Econometrics Letters 2012, 117, 10–13) have different perfor...

This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel MitISEM. The basic MitISEM algorithm provides an automatic and flexible method to approximate a non-elliptical target density using adaptive mixtures...

This study investigates the oversubscription in public issues of Small and Medium Enterprises in India, across different quantiles. This study further explores the non-linearity in factors through machine learning. Utilising data from 1014 IPOs durin...

Science and technology parks (STPs) are curated locations where new technology-based firms (NTBFs) and other SMEs and firms can conglomerate and promote a culture of innovation. Overall, the aim is to construct a sustainable high-value tech entrepren...

This work demonstrates for the first time the application of network topology of variance decompositions in analyzing the connectedness of chemical plant process variable oscillations arising from disturbances and faults. Specifically, the time-based...

While the positive economic impact of tourism cannot be denied, it has also a number of negative side effects. The article focuses on the residents’ perception regarding the tourist activity and on identifying the factors influencing their acce...

In this study, we review recent research on how digital payment technologies (DPTs) promote financial inclusion (FI) across the world. Drawing on empirical studies from the past decade, we show that digital payment systems have helped reduce financia...

The Pareto distribution has been widely used to model income distribution and inequality. The tail index and the Gini index are typically computed by iteration using Maximum Likelihood and are usually interpreted in terms of the Lorenz curve. We deri...

High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advance...

We discuss some conceptual and practical issues that arise from the presence of global energy balance effects on station level adjustment mechanisms in dynamic panel regressions with climate data. The paper provides asymptotic analyses, observational...