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Time-varying parameters vector autoregressions with multivariatestochastic volatility

具有多元随机波动性的时变参数向量自回归

基本信息

批准号：
394413895
负责人：
Professor Dr. Roman Liesenfeld
金额：
--
依托单位：
Universidade Federal de Santa Catarina (UFSC)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2018
资助国家：
德国
起止时间：
2017-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/394413895?language=en
关键词：
Time varying parameters vector autoregressions

项目摘要

Vector autoregressions (VARs) are widely used to provide a systematic way to capture richdynamics in multiple time series, and thus have become a widely used tool for modelingand forecasting in macroeconomics and nance. In recent years, VARs with time-varyingparameters and stochastic volatility (TVP-VARs) have received increasing attention becauseof an ever-growing body of evidence questioning the usual assumption of stable parameters,especially, in time series where data encompass several decades, increasing the probabilityof changes in the dynamic structure of the underlying stochastic process. Like standardVARs, TVP-VARs oer an approach to data description, forecasting, structural inferenceand policy analysis, and as such can be used to analyze a large number of economic problems.However, standard multivariate stochastic volatility (MSV) specications used for the timevaryingcovariance matrix of the TVP-VAR innovations are not invariant w.r.t. the orderingof the time series variables in the VAR system so that this ordering undesirably aects theinference results.In this project we consider ordering-invariant yet exible MSV models based on Wishartprocesses. They have become popular in nancial applications but the statistical inferencerequired for their empirical application remains to be a challenging task. We aim at developingMonte-Carlo based inference procedures for a maximum likelihood analysis of thoseWishart MSV models and then will extend the inference procedures for the analysis of TVPVARswith Wishart MSV specications for the covariance matrix of the innovations. Thedeveloped methods will be implemented for thorough nancial and marcoeconomic applications.

向量自回归（VAR）被广泛用于提供一种系统的方法来捕捉多个时间序列中的丰富动态，因此已成为宏观经济和金融领域中广泛使用的建模和预测工具。近年来，具有时变参数和随机波动率的VAR（TVP-VAR）受到越来越多的关注，因为越来越多的证据质疑通常的稳定参数假设，特别是在数据包含几十年的时间序列中，增加了潜在随机过程动态结构变化的概率。与标准VAR一样，TVP-VAR也是一种数据描述、预测、结构推断和政策分析的方法，因此可以用来分析大量的经济问题。VAR系统中时间序列变量的排序问题，使得这种排序对推理结果产生了不利的影响。在这个项目中，我们考虑了基于Wishart过程的排序不变但灵活的MSV模型。他们已经成为流行的金融应用，但统计推断所需的经验应用仍然是一个具有挑战性的任务.我们的目标是开发基于Monte-Carlo的推理程序的最大似然分析thoseWishartMSV模型，然后将扩展的推理程序的分析TVPVARsWishartMSV规格的协方差矩阵的创新。开发的方法将实施彻底的金融和宏观经济应用。