Arch, Cointegration and Common Features: Theory and Application

这是一个基于成就的项目更新， 开发了协整模型及相关模型和检验。 现在几乎每一个领域都在使用协整模型。 经济学领域，以测试之间的经济关系 不同经济变量的时间序列数据。 这些 研究通常使用单变量时间序列模型， 最多两个或三个不同时间的多变量模型 系列. 这是大多数经济应用的主要缺点。 这笔赠款将允许研究人员继续进行研究 在以前的多变量数据库赠款下开始， 协整分析 此外，该项目还开发和 应用一种新的统计程序，称为共同特征， 经济和金融的问题。 会议将在 1992年4月，在加州的拉霍亚， 波动率模型及其在金融中的应用。 经济时间序列具有许多鲜明的特征。 一般来说，它们表现出序列相关性、趋势性、季节性， 通常是异方差性、偏度、峰度和各种其他 功能. 为了检测数据中的每个特征， 设置，各种各样的测试都是可用的，每个测试都需要 作为空的替代品的特定功能 假设该特征不存在于数据中。 下 在之前的NSF资助下，研究人员开发了一种新的 统计程序称为共同特征，允许 分析师确定两个或多个数据集是否共享相同的 与众不同的特征 该程序用于显示 有经验证据表明， 主要工业国家的经济周期。 的程序 将得到推广和普及。 它将被用来检查 美国各部门的产出，以确定各部门是否共同变动 在商业周期中。 该程序将用于确定 美国境内的地区是否会一起移动。 国际 将分析资本市场的数据，以确定 特征是共同的股票市场的几个街区， 国家 这项研究应该提供新的见解， 国际资本市场波动的性质。

This is an accomplishment based renewal of a project that developed ARCH and related models and tests for cointegration. Models of ARCH and cointegration are now used in almost every area of economics to test for economic relationships among different economic variables using time series data. These studies typically use univariate time series models or multivariate models with at most two or three different time series. This is a major drawback for most economic applications. This grant will permit the investigator to continue the research started under the previous grant on multivariate ARCH and cointegration analysis. In addition the project develops and applies a new statistical procedure called common features to problems in economics and finance. A conference will be held in April, 1992 in La Jolla, California on new developments in volatility models and applications to finance. Economic time series have many distinctive characteristics. Generally, they exhibit serial correlation, trends, seasonality, often heteroskedasticity, skewness, kurtosis, and various other features. In order to detect each of these features in a data set, a variety of tests are available each of which takes the particular feature in question as the alternative to the null hypothesis that the feature is not present in the data. Under its previous NSF grant, the investigator developed a new statistical procedure called common features that permits the analyst to determine if two or more data sets share the same distinctive characteristics. This procedure was used to show that there was empirical evidence of a common international business cycle for the major industrial countries. The procedure will be extended and generalized. It will be used to examine sectoral output in the U.S. to see whether sectors move together over the business cycle. The procedure will be used to determine whether regions within the U.S. move together. International data on capital markets will be analyzed to determine what features are common to the equity markets for several blocks of countries. This research should provide new insights into the nature of the volatility in international capital markets.