Stochastic Economic Variables Having Equilibria as Simple Attractors

具有均衡作为简单吸引子的随机经济变量

• 批准号: 8704669
• 负责人: Clive Granger
• 金额: $ 10.23万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-08-01 至 1990-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8704669&HistoricalAwards=false
• 关键词: Stochastic; Economic; Variables; Having; Equilibria

This project makes an important contribution to the statistical theory concerning the analysis of economic time series data. Traditionally, models considered by time series analysts have concentrated on a few important economic variables and incorporated complicated lag structures of those variables. Structure of the economy was inferred from the data by examining the way the economy evolved and changed through time. This is in contrast to another form of economic modelling, which includes many variables in a sharply specified mathematical structure consisting of many equations. Both techniques have their advantages and disadvantages, and this project is part of an ongoing attempt to meld the two modelling strategies into a single coherent, and statistically consistent framework. Much empirical research in macroeconomics has shown that economic time series data typically include non-stationary processes such as trends. Different data series, for example national income data, GNP data, and price series, might include a common trend or tendency to grow over time. Thus these data are statistically linked with each other in the long run. To gain any meaningful insight into the actual movements of the variables in the economy, one must purge the data of the influence of non-stationary processes. In the past, achieving stationarity has been especially difficult if an analytical model included several data series each containing similar growth tendencies. Professor Granger has developed a statistical characterization of time series known as co-integration in which a linear combination of data series is itself stationary. This project makes a significant contribution to the existing literature in that it extends the idea of co-integration of time series to allow for nonlinear common trends, and offers an economic interpretation of data fluctuations as movements away from economic equilibrium. For example the price of an agricultural product such as tomatoes will vary from one section of the country to another, but will not vary a great deal, at least not in the long run. If the price of a product differs greatly from one location to another, market forces will act to eliminate the price disparities and reach an equilibrium. Professor Granger empirically identifies the equilibrium concept as a point to which the data in the series seem to gravitate. This point is known as an attractor, and the task of the econometrician is to isolate possible attractors in a group of data sets that the economic theory has suggested should be considered in the analysis and to confirm their importance. This work significantly increases the strength of the bond between economic theory and empirical analysis.

该项目对统计理论做出了重要贡献 关于经济时间序列数据的分析。 传统上， 时间序列分析师考虑的模型集中在几个方面 重要经济变量和复杂滞后 这些变量的结构。 经济结构推断 通过研究经济发展和变化的方式 穿越时空 这与另一种形式的经济 模型，其中包括许多变量在一个尖锐的指定 由许多方程组成的数学结构。 这两种技术 有其优点和缺点，这个项目是一部分， 正在进行的将两种建模策略合并为一种 连贯一致的统计框架。 许多宏观经济学的实证研究表明， 系列数据通常包括诸如趋势的非平稳过程。 不同的数据系列，例如国民收入数据、国民生产总值数据和 价格系列，可能包括一个共同的趋势或增长趋势， 时间 因此，这些数据在统计上相互关联， 长远 为了获得任何有意义的洞察到实际的运动， 经济中的变量，必须清除数据的影响 非平稳过程。 在过去，实现稳定性 如果一个分析模型包含多个数据， 每个系列都包含类似的增长趋势。 格兰杰教授 开发了一种时间序列的统计特征， 协整，其中数据序列的线性组合本身是 静止的 该项目为现有的 它扩展了时间协整的概念， 系列允许非线性共同趋势，并提供了一个经济 将数据波动解释为偏离经济 均衡 例如，农产品的价格， 西红柿会有所不同，从一个国家的一部分到另一个，但 不会有太大的变化，至少从长远来看不会。 如果 产品的价格从一个地方到另一个地方差别很大， 市场力量将采取行动消除价格差异并达到 均衡 格兰杰教授根据经验确定了均衡 这是一个概念，作为一个点，该系列中的数据似乎吸引。 这一点被称为吸引子， 一个计量经济学家的工作是从一组数据中分离出可能的吸引子 经济学理论所建议的应该被考虑在 分析并确认其重要性。 这项工作意义重大 增强了经济理论与 实证分析