A COMPOSITE LIKELIHOOD APPROACH FOR DYNAMIC STRUCTURAL MODELS

• 成果类型: Article
• 署名作者: Canova, Fabio; Matthes, Christian
• 署名单位: Indiana University System; Indiana University Bloomington; BI Norwegian Business School
• 刊物名称: ECONOMIC JOURNAL
• ISSN/ISSBN: 0013-0133
• DOI: 10.1093/ej/ueab004
• 发表日期: 2021
• 页码: 2447-2477
• 关键词: bayesian-inference; equilibrium-models; identification; FRAMEWORK; frictions; priors

摘要

We explain how to use the composite likelihood function to ameliorate estimation, computational and inferential problems in dynamic stochastic general equilibrium models. We combine the information present in different models or data sets to estimate the parameters common across models. We provide intuition for why the methodology works and alternative interpretations of the estimators we construct and of the statistics we employ. We present a number of situations where the methodology has the potential to resolve well-known problems and to provide a justification for existing practices that pool different estimates. In each case, we provide an example to illustrate how the approach works and its properties in practice.