---
title: Bayesian ANN for Efficiency Analysis
url: https://www.ml-quant.com/papers/repec/eee-econom-v-236-y-2023-i-2-s0304407623002075/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:econom:v:236:y:2023:i:2:s0304407623002075
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0304407623002075%3Bh%3Drepec%3Aeee%3Aeconom%3Av%3A236%3Ay%3A2023%3Ai%3A2%3As0304407623002075
featured: 2023-09-14
citations: unknown
topic: Econometrics & Forecasting
---


# Bayesian ANN for Efficiency Analysis

The paper introduces a novel method for frontier estimation in econometrics, merging Data Envelopment Analysis and Stochastic Frontier Analysis using Bayesian artificial neural networks, and validates its efficiency with Monte Carlo experiments and a dataset of large US banks.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0304407623002075%3Bh%3Drepec%3Aeee%3Aeconom%3Av%3A236%3Ay%3A2023%3Ai%3A2%3As0304407623002075
- Identifier: RePEc:eee:econom:v:236:y:2023:i:2:s0304407623002075
- Released: 2023-09-14
- First featured: Quant Letter No. 15 (2023-09-14): https://www.ml-quant.com/issues/2023-09-14/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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