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Forecasting macroeconomic variables in data-rich environments

Medeiros, Marcelo C.; Vasconcelos, Gabriel F. R. - In: Economics letters 138 (2016), pp. 50-52

Persistent link: https://www.econbiz.de/10011615474

A bibliometric analysis of machine learning econometrics in asset pricing

Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of risk and financial management : JRFM 15 (2022) 11, pp. 1-17

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 econometrics, they address, and may be better...

Persistent link: https://www.econbiz.de/10013475217

A novel credit model risk measure : do more data lead to lower model risk?

Yoshida, Valter T.; Schiozer, Rafael Felipe; Genaro, Alan de - In: The quarterly review of economics and finance 100 (2025), pp. 1-15

Persistent link: https://www.econbiz.de/10015405538

Machine learning methods for inflation forecasting in Brazil : new contenders versus classical models

Araújo, Gustavo Silva; Gaglianone, Wagner Piazza - In: Latin American journal of central banking : LAJCB 4 (2023) 2, pp. 1-29

In this paper, we explore machine learning (ML) methods to improve inflation forecasting in Brazil. An extensive out-of-sample forecasting exercise is designed with multiple horizons, a large database of 501 series, and 50 forecasting methods, including new ML techniques proposed here,...

Persistent link: https://www.econbiz.de/10014382916

Financial big data solutions for state space panel regression in interest rate dynamics

Toczydlowska, Dorota; Peters, Gareth - In: Econometrics : open access journal 6 (2018) 3, pp. 1-45

A novel class of dimension reduction methods is combined with a stochastic multi-factor panel regression-based state-space model in order to model the dynamics of yield curves whilst incorporating regression factors. This is achieved via Probabilistic Principal Component Analysis (PPCA) in which...

Persistent link: https://www.econbiz.de/10011887659

Forecasting the returns of cryptocurrency : a model averaging approach

Xiao, Hui; Sun, Yiguo - In: Journal of risk and financial management : JRFM 13 (2020) 11/278, pp. 1-15

estimators to explain the cryptocurrencies´ returns. We further introduce a novel model averaging approach or the shrinkage …

Persistent link: https://www.econbiz.de/10012388749

Optimal and robust combination of forecasts via constrained optimization and shrinkage

Roccazzella, Francesco; Gambetti, Paolo; Vrins, Frédéric - In: International journal of forecasting 38 (2022) 1, pp. 97-116

Persistent link: https://www.econbiz.de/10013347741

Asymmetric conjugate priors for large Bayesian VARs

Chan, Joshua - In: Quantitative economics : QE ; journal of the … 13 (2022) 3, pp. 1145-1169

Large Bayesian VARs are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the …, however, at the expense of modeling flexibility, as it rules out cross‐variable shrinkage, that is, shrinking coefficients on … can accommodate cross‐variable shrinkage, while maintaining many useful analytical results, such as a closed …

Persistent link: https://www.econbiz.de/10013382075

Forecast combination under heavy-tailed errors

Cheng, Gang; Wang, Sicong; Yang, Yuhong - In: Econometrics : open access journal 3 (2015) 4, pp. 797-824

Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our...

Persistent link: https://www.econbiz.de/10011411497

Hybridising neurofuzzy model the seasonal autoregressive models for electricity price forecasting on Germany's spot market

Paraschiv, Dorel Mihai; Bălășoiu, Narciz; Ben Amor, … - In: Amfiteatru economic : an economic and business research … 25 (2023) 63, pp. 463-478

Electricity price forecasting has become an area of increasing relevance in recent years. Despite the growing interest in predictive algorithms, the challenges are difficult to overcome given the restricted access to relevant data series and the lack of accurate metrics. Multiple models have...

Persistent link: https://www.econbiz.de/10014464238