Econometric Modeling of Processes with Varying Structural Parameters

具有变化结构参数的过程的计量经济学建模

• 批准号: 8705884
• 负责人: Robert Engle
• 金额: $ 11.7万
• 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 1990-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8705884&HistoricalAwards=false
• 关键词: Econometric; Modeling; Processes; Varying; Structural

This project continues a successful line of econometric research on a type of model especially suited for time series analysis, in particular for examining financial sector price movements. Traditional statistical methods for time series analysis assume that the variances of forecast errors remain constant from one observation to the next. Professor Engle and his colleagues have developed a class of models know as Autoregressive Conditional Heteroskedasticity (ARCH) models which relax this restrictive assumption. In the ARCH model the variances of a variable are themselves stochastic and conditioned on the information contained in the entire variable set. These models have been used very successfully in analyzing asset price movements, volatility in the stock market, in forecasting inflation, and in measuring risk. In this project Professor Engle extends his past work to include multivariate time series models. He allows the variances to change over time in a manner that does not decrease the generality of the model, but results in a parsimonious specification. Also included in this work is the concept of co-integration, which is a statistical characterization of several series of data which might reasonably contain common trends or growth tendencies. This is an important extension in that such models have a straightforward economic interpretation. Professor Engle also empirically validates his theoretical work by applying the ARCH specification to data on asset prices in foreign exchange markets, interest rates, options contracts, and equity prices.

这个项目延续了一条成功的计量经济学研究路线， 一种特别适合于时间序列分析的模型， 特别是研究金融部门的价格变动。 时间序列分析的传统统计方法假设， 预测误差的方差在一次观测中保持不变 到下一个。 恩格尔教授和他的同事们开发了一种 自回归条件异方差模型 （3）放宽这一限制性假设的模型。 在足弓 模型变量的方差本身是随机的， 以整个变量集中包含的信息为条件。 这些模型在资产价格分析中得到了成功的应用 股市的变动、波动、预测通货膨胀， 和衡量风险。 在这个项目中，恩格尔教授扩展了他过去的工作， 多变量时间序列模型 他允许方差变化 以一种不降低一般性的方式， 模型，但结果在一个吝啬的规格。 中还包括 这项工作是协整的概念，这是一个统计 几个系列的数据，可能合理的表征 包含共同的趋势或增长趋势。 这是一个重要 这种模型具有直接的经济学意义， 解释。 恩格尔教授还从经验上验证了他的 理论工作，通过对资产数据应用数据库规范 外汇市场价格、利率、期权合约、 和股票价格。