Topics in Economic Dynamics and Time Series

经济动态和时间序列主题

• 批准号: 0112359
• 负责人: Lars Hansen
• 金额: $ 22.2万
• 依托单位: National Opinion Research Center
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-07-15 至 2005-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0112359&HistoricalAwards=false
• 关键词: Topics; Economic; Dynamics; Time; Series

Research will be completed on three different topics:First, the project develops a new approach to characterizing nonlinearities in weakly dependent time series. Nonlinear principal component decompositions are used to represent the evolution of continuous-time Markov processes and to summarize the correlation structure. These decompositions help us understand better the evolution of relative prices across locations and the nonlinearities induced by transaction or transport costs. This work draws on three literatures: functional principal component analysis; quadratic-form modeling of Markov processes; and nonlinear, stochastic modeling of real exchange rates. Nonlinear models that are strongly dependent or nonstationary are also considered.Second, the investigator continues his study of economies in which private agents and policy-makers confront model misspecification. The project explores and implements solution methods for nonlinear, stochastic equilibrium models. It studies monetary models in which the misspecification concerns of macroeconomic policy makers differ from those of private agents. It builds from insights in the time-series econometrics literature on misspecification and filtering to investigate alternative formulations of robustness in the decision-making of private agents. Finally, the project studies connections between robustness, adaptive learning and information- based models of inertia.Third, the project examines the observable implications of dynamic models under uncertainty with human capital and portfolio investment. Implications for time series and cross sections of asset returns are deduced and tested. The role of heterogeneity in the stochastic structure of labor income also is considered. Finally, the project investigates the role of financial market structures on the shadow valuation of human capital.

研究将在三个不同的主题上完成：首先，该项目开发了一种新的方法来表征弱相关时间序列中的非线性。非线性主成分分解被用来表示连续时间马尔可夫过程的演化，并总结相关结构。这些分解有助于我们更好地理解不同地点的相对价格的演变以及交易或运输成本引起的非线性。这项工作借鉴了三个文献：功能主成分分析;二次形式的马尔可夫过程建模;和非线性，随机建模的真实的汇率。强相关或非平稳的非线性模型也被认为是。第二，调查员继续他的经济研究中，私人代理人和政策制定者面临的模型错误。该项目探索并实现非线性随机平衡模型的求解方法。 它研究货币模型，其中宏观经济政策制定者的错误设定关注不同于私人代理人。它建立在时间序列计量经济学文献中的见解，误指定和过滤，以调查私营代理人的决策中的鲁棒性的替代配方。最后，研究鲁棒性、自适应学习和基于信息的惯性模型之间的联系。第三，研究不确定性下动态模型对人力资本和证券投资的可观察影响。推导和检验了资产收益率的时间序列和横截面的含义。本文还考虑了异质性在劳动收入随机结构中的作用。最后，本项目研究金融市场结构对人力资本影子估值的作用。