Search Results - subject:"gaussian process"

Physics-informed Gaussian process regression for states estimation and forecasting in power grids

Tartakovsky, Alexandre M.; Ma, Tong; Barajas-Solano, … - In: International journal of forecasting 39 (2023) 2, pp. 967-980

Persistent link: https://www.econbiz.de/10014465184

Estimation and inference in a high-dimensional semiparametric Gaussian copula vector autoregressive model

Fan, Yanqin; Han, Fang; Park, Hyeonseok - In: Journal of econometrics 237 (2023) 1, pp. 1-28

Persistent link: https://www.econbiz.de/10014471479

Machine learning for multiple yield curve markets : fast calibration in the Gaussian affine framework

Gümbel, Sandrine; Schmidt, Thorsten - In: Risks : open access journal 8 (2020) 2/50, pp. 1-18

and many challenges for the multi-curve markets in a Vasiček framework. The Gaussian process regression is implemented … to study the chances and challenges of the application of machine learning techniques for this. We employ Gaussian … process regression, a machine learning methodology having many similarities with extended Kálmán filtering, which has been …

Persistent link: https://www.econbiz.de/10012292851

Optimal computation budget allocation with Gaussian process regression

Hu, Mingjie; Xu, Jie; Chen, Chun-hung; Hu, Jian-Qiang - In: European journal of operational research : EJOR 322 (2025) 1, pp. 147-156

Persistent link: https://www.econbiz.de/10015410151

A bibliometric analysis of machine learning econometrics in asset pricing

Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of Risk and Financial Management 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/10014332691

Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates

Barde, Sylvain - 2022

model is replaced by one generated by a surrogate model. This is provided by a sparse variational Gaussian process, chosen …

Persistent link: https://www.econbiz.de/10014480466

Examining the vintage effect in hedonic pricing using spatially varying coefficients models: A case study of single-family houses in the Canton of Zurich

Dambon, Jakob A.; Fahrländer, Stefan Sebastian; … - In: Swiss Journal of Economics and Statistics 158 (2022) 1, pp. 1-14

This article examines the spatially varying effect of age on single-family house (SFH) prices. Age has been shown to be a key driver for house depreciation and is usually associated with a negative price effect. In practice, however, there exist deviations from this behavior which are referred...

Persistent link: https://www.econbiz.de/10013205819

Examining the vintage effect in hedonic pricing using spatially varying coefficients models : a case study of single-family houses in the Canton of Zurich

Dambon, Jakob A.; Fahrländer, Stefan S.; Karlen, Saira; … - In: Swiss journal of economics and statistics 158 (2022) 1, pp. 1-14

Persistent link: https://www.econbiz.de/10012793516

Neighborhood-Based Information Costs

Hébert, Benjamin; Woodford, Michael - 2022

We derive a new cost of information in rational inattention problems, the neighborhood-based cost functions, starting from the observation that many settings involve exogenous states with a topological structure. These cost functions are uniformly posterior-separable and capture notions of...

Persistent link: https://www.econbiz.de/10013314321

Forecasting US inflation using Bayesian nonparametric models

Clark, Todd E.; Huber, Florian; Koop, Gary; Marcellino, … - 2022

Persistent link: https://www.econbiz.de/10013277506