Collaborative Research on Measurement with Theory: A Bayesian Approach to Dynamic Macroeconomics

测量与理论的合作研究：动态宏观经济学的贝叶斯方法

• 批准号: 9422828
• 负责人: David DeJong
• 金额: $ 16.95万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-06-01 至 1998-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9422828&HistoricalAwards=false
• 关键词: Collaborative; Research; Measurement; Theory; Bayesian

SBR-9422828 David DeJong In conducting empirical research, guidance from theory is extremely valuable. Yet in macroeconomics, as theoretical models have become increasingly complex and stylized, such guidance has become difficult to incorporate in a formal statistical sense. This project develops and then implements a coherent statistical framework for combining theoretical and empirical information. The framework is Bayesian and enables the formal yet probabilistic incorporation of theoretical restrictions (e.g., cross-equation restrictions) in working with reduced-form models. A distinctive feature of the approach proposed here is that theoretical models are viewed as sources of prior information which can then be formally combined with data information via Bayes' Rule. This is particularly useful in working with reduced-form models such as vector autoregressions (VARs), where researchers frequently have strong views about the parameterization of the theoretical model but only weak views about the parameterization of the VAR. The new framework also enables economists to incorporate prior information about parameters values in the estimation of structural models. This stands in marked contrast with the current practice of incorporating such information only informally and implicitly. The result of this practice is inference without a firm statistical basis. The methodology is then used in a variety of empirical projects: an analysis of the source of shocks which have driven the U.S. post-war business cycle; an analysis of competing sources of economic growth; construction and estimation of a fully specified nominal business cycle model (which includes monetary and fiscal policy variables) for the purpose of conducting policy analysis; an analysis of the empirical viability of simple heterogeneous agent models with incomplete markets; and an analysis of the usefulness of theoretical asset pricing models in the modeling of financial time series.

SBR-9422828大卫德容 在进行实证研究时，理论的指导是非常有价值的。 然而，在宏观经济学中，随着理论模型变得越来越复杂和程式化，这种指导已经很难纳入正式的统计意义。 该项目开发并实施了一个连贯的统计框架，用于结合理论和经验信息。 该框架是贝叶斯的，并且使得能够正式但概率地并入理论限制（例如，交叉方程限制）与简化形式的模型。 这里提出的方法的一个显着特点是，理论模型被视为先验信息的来源，然后可以通过贝叶斯规则与数据信息正式相结合。 这在使用简化形式模型（如向量自回归（VAR））时特别有用，研究人员经常对理论模型的参数化有强烈的看法，但对VAR的参数化只有微弱的看法。 新的框架还使经济学家能够将有关参数值的先验信息纳入结构模型的估计中。 这与目前仅以非正式和含蓄方式纳入此类信息的做法形成鲜明对比。 这种做法的结果是没有坚实统计基础的推论。 然后，该方法被用于各种实证项目：分析推动美国战后商业周期的冲击来源;分析经济增长的竞争来源;构建和估计一个完全指定的名义商业周期模型（包括货币和财政政策变量），以进行政策分析;分析不完全市场的简单异质代理模型的经验可行性;分析理论资产定价模型在金融时间序列建模中的有用性。