Similar Search Results

Bridging the divide? : Bayesian artificial neural networks for frontier efficiency analysis

Tsionas, Efthymios G.; Parameter, Christopher F.; … - 2021

Persistent link: https://www.econbiz.de/10013256898

Bayesian Artificial Neural Networks for frontier efficiency analysis

Tsionas, Efthymios G.; Parmeter, Christopher F.; … - In: Journal of econometrics 236 (2023) 2, pp. 1-14

Persistent link: https://www.econbiz.de/10014365509

Bayesian artificial neural networks for frontier efficiency analysis

Tsionas, Efthymios G.; Parmeter, Christopher F.; … - 2023 - Substantially revised version

Persistent link: https://www.econbiz.de/10015066491

Input space to neural network based load forecasters

Alves da Silva, Alexandre P.; Ferreira, Vitor H.; … - In: International journal of forecasting 24 (2008) 4, pp. 616-629

Persistent link: https://www.econbiz.de/10003808331

A comparison of neural networks and Bayesian MCMC for the Heston model estimation (forget statistics – machine learning is sufficient!)

Witzany, Jiří; Fičura, Milan - 2023

Persistent link: https://www.econbiz.de/10014338462

Neural nets for indirect inference

Creel, Michael D. - 2016

Persistent link: https://www.econbiz.de/10011607329

Inference using simulated neural moments

Creel, Michael D. - In: Econometrics : open access journal 9 (2021) 4, pp. 1-15

This paper studies method of simulated moments (MSM) estimators that are implemented using Bayesian methods, specifically Markov chain Monte Carlo (MCMC). Motivation and theory for the methods is provided by Chernozhukov and Hong (2003). The paper shows, experimentally, that confidence intervals...

Persistent link: https://www.econbiz.de/10012642418

Inference using simulated neural moments

Creel, Michael D. - 2020 - This version: November 2020

Persistent link: https://www.econbiz.de/10012431141

Essays on neural network sampling methods and instrumental variables

Hoogerheide, Lennart Frank - 2006

Persistent link: https://www.econbiz.de/10003338640

Conflict analysis using Bayesian neural networks and generalized linear models

Iswaran, N.; Percy, D. F. - In: Journal of the Operational Research Society : OR 61 (2010) 2, pp. 332-341

Persistent link: https://www.econbiz.de/10003938168