Results 111 to 120 of about 28,836 (304)

Addressing non-stationarity with stochastic trend in the context of limited time series data: An experimental survey in healthcare analytics

Applied Computer Science
Stationarity is a fundamental assumption in time series modeling that underlies reliable statistical inference and forecasting. Time series data can be found in many domains, including industry, engineering, finance, economics, epidemiology, and health ...
Apollinaire BATOURE BAMANA, Yannick SOKDOU BILA LAMOU, David Jaures FOTSA-MBOGNE, Mahdi SHAFIEE KAMALABAD   +3 more
doaj   +1 more source

Using DSGE and Machine Learning to Forecast Public Debt for France

Journal of Forecasting, EarlyView.
ABSTRACT Forecasting public debt is essential for effective policymaking and economic stability, yet traditional approaches face challenges due to data scarcity. While machine learning (ML) has demonstrated success in financial forecasting, its application to macroeconomic forecasting remains underexplored, hindered by short historical time series and ...
Emmanouil Sofianos, Thierry Betti, Theophilos Papadimitriou, Amélie Barbier‐Gauchard, Periklis Gogas   +4 more
wiley   +1 more source

Forecasting Volatility of Commodity, Currency, and Stock Markets: Evidence From Markov‐Switching Multifractal Models

Journal of Forecasting, EarlyView.
ABSTRACT This paper adopts a bivariate Markov‐switching multifractal (BMSM) model to reexamine comovement in SV between commodity, foreign exchange (FX), and stock markets. After the 2007–2008 global financial crisis understanding volatility linkages and the correlation structure between these markets becomes very important for risk analysts, portfolio
Ruipeng Liu, Mawuli Segnon, Oguzhan Cepni, Rangan Gupta   +3 more
wiley   +1 more source

Modelling induced innovation for the low-carbon energy transition: a menu of options

Environmental Research Letters
Induced innovation is a multi-faceted process characterized by interaction between demand-pull forces, path-dependent self-reinforcing change, and the cost reduction of technology that occurs with cumulative deployment.
Roberto Pasqualino, Cristina Peñasco, Peter Barbrook-Johnson, Fernanda Senra De Moura, Sergey Kolesnikov, Sarah Hafner, Femke J M M Nijsse, Francesco Lamperti, Ben Hinder, Yaroslav Melekh, Simon Sharpe, Aled W Jones, Laura Díaz Anadón, Timothy M Lenton, Michael Grubb   +14 more
doaj   +1 more source

Asymptotic Analysis of Poverty Dynamics via Feller Semigroups

Mathematics
Poverty is a multifaceted phenomenon impacting millions globally, defined by a deficiency in both material and immaterial resources, which consequently restricts access to satisfactory living conditions. Comprehensive poverty analysis can be accomplished
Lahcen Boulaasair, Mehmet Yavuz, Hassane Bouzahir   +2 more
doaj   +1 more source

Finite dimensional Markovian realizations for stochastic volatility forward rate models [PDF]

We consider forward rate rate models of HJM type, as well as more general infinite dimensional SDEs, where the volatility/diffusion term is stochastic in the sense of being driven by a separate hidden Markov process.
Björk, Tomas, Svensson, Lars, Landén, Camilla   +2 more
core  

DSGE Model Forecasting: Rational Expectations Versus Adaptive Learning

Journal of Forecasting, EarlyView.
ABSTRACT This paper compares within‐sample and out‐of‐sample fit of a DSGE model with rational expectations to a model with adaptive learning. The Galí, Smets, and Wouters model is the chosen laboratory using quarterly real‐time euro area data vintages, covering 2001Q1–2019Q4.
Anders Warne
wiley   +1 more source

Pathways to randomness in the economy: Emergent nonlinearity and chaos in economics and finance [PDF]

This paper: (1) Gives a general argument why research on nonlinear science in general and chaos in particular is important in economics and finance. (2) Puts forth two definitions of stochastic nonlinearity (IID-Linearity and MDS-Linearity) for nonlinear
William A. Brock
core  

Nowcasting World Trade With Machine Learning: A Three‐Step Approach

Journal of Forecasting, EarlyView.
ABSTRACT We nowcast world trade using machine learning, distinguishing between tree‐based methods (random forest and gradient boosting) and their linear‐regression‐based counterparts (macroeconomic random forest and gradient boosting—linear). While much less used in the literature, the latter are found to outperform not only the tree‐based techniques ...
Menzie Chinn, Baptiste Meunier, Sebastian Stumpner   +2 more
wiley   +1 more source

Particle Filters for Markov Switching Stochastic Volatility Models [PDF]

This paper proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain.
Boda Kang, Yun Bao, Carl Chiarella
core