TITLE:
Application of Bayesian Econometrics in Policy Evaluation
AUTHORS:
Haiyue Wang
KEYWORDS:
Bayesian Econometrics, Policy Evaluation, Parameter Uncertainty, Model Selection, Causal Inference
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.12 No.11,
November
18,
2024
ABSTRACT: This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, including the concepts of Bayesian inference, prior distributions, and posterior distributions. Through systematic analysis, the study constructs a theoretical framework for applying Bayesian methods in policy evaluation. The research finds that Bayesian methods have multiple theoretical advantages in policy evaluation: Based on parameter uncertainty theory, Bayesian methods can better handle uncertainty in model parameters and provide more comprehensive estimates of policy effects; from the perspective of model selection theory, Bayesian model averaging can reduce model selection bias and enhance the robustness of evaluation results; according to causal inference theory, Bayesian causal inference methods provide new approaches for evaluating policy causal effects. The study also points out the complexities of applying Bayesian methods in policy evaluation, such as the selection of prior information and computational complexity. To address these complexities, the study proposes hybrid methods combining frequentist approaches and suggestions for developing computationally efficient algorithms. The research also discusses theoretical comparisons between Bayesian methods and other policy evaluation techniques, providing directions for future research.