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

9422828大卫·德容在进行实证研究时，从理论上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，这样的指导已经很难纳入正式的统计意义上。该项目开发并实施了一个连贯的统计框架，将理论信息和经验信息结合起来。该框架是贝叶斯的，并允许在使用简化形式的模型时正式但概率地纳入理论限制(例如，交叉方程限制)。该方法的一个显著特点是将理论模型视为先验信息的来源，然后通过贝叶斯规则将先验信息与数据信息形式化地结合在一起。这在处理向量自回归(VAR)等简化模型时尤其有用，在这些模型中，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在结构模型的估计中纳入有关参数值的先验信息。这与目前仅非正式和含蓄地纳入这类信息的做法形成鲜明对比。这种做法的结果是推论没有坚实的统计基础。这种方法随后被用于各种实证项目：对推动美国战后商业周期的冲击来源的分析；对经济增长的竞争来源的分析；为进行政策分析而构建和估计完全特定的名义商业周期模型(其中包括货币和财政政策变量)；分析具有不完全市场的简单异质代理模型的经验可行性；以及理论资产定价模型在金融时间序列建模中的有用性分析。

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.