Arch, Cointegration and Common Features: Theory and Application

这是一个基于成就的项目的更新，该项目开发了ARCH和相关的协整模型和测试。ARCH和协整模型现在几乎被用于经济学的每个领域，以使用时间序列数据来检验不同经济变量之间的经济关系。这些研究通常使用单变量时间序列模型或最多具有两到三个不同时间序列的多变量模型。对于大多数经济应用来说，这是一个重大缺陷。这笔赠款将允许研究人员继续根据前一笔赠款开始的关于多变量ARCH和协整分析的研究。此外，该项目还开发并应用了一种新的统计程序，称为经济和金融问题的共同特征。1992年4月将在加利福尼亚州拉荷亚举行一次会议，讨论波动率模型和金融应用的新发展。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，往往具有异方差、偏度、峰度等各种特征。为了在数据集中检测这些特征中的每一个，可以使用各种测试，其中每个测试都将所讨论的特定特征作为关于该特征不存在于数据中的零假设的替代。在之前的NSF拨款下，研究人员开发了一种名为公共特征的新统计程序，允许分析师确定两个或更多数据集是否具有相同的独特特征。这一程序被用来表明，有经验证据表明，主要工业国存在共同的国际商业周期。该程序将得到推广和推广。它将被用来检查美国的部门产出，看看各部门是否在整个商业周期中一起行动。这一程序将被用来确定美国境内的地区是否一起行动。将对资本市场的国际数据进行分析，以确定几个国家的股票市场有哪些共同特征。这项研究应该会对国际资本市场波动的性质提供新的见解。

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.