{"title":"Volatility in Macroeconomics","authors":"Sajjadur Rahman","doi":"10.4172/2168-9458.1000E115","DOIUrl":null,"url":null,"abstract":"Copyright: © 2012 Rahman S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Volatility has gained lots of attention in finance literature, as, for example, in studies of the relation between stock market returns and risk, but it has been a much lower priority for applied macroeconomists. In general, the existing macroeconometrics research is concerned mainly with the first moment (or mean) of the variables, while systematically ignoring the second moment (or variance). However, a correct specification of variance is still important for two reasons, as is explained by Hamilton and Herrera [1]. First, the test of hypothesis under misspecified variance is invalid. Second, it is possible to improve the efficiency of the conditional mean estimates by incorporating the observed feature of heteroscedasticity into the estimation process.","PeriodicalId":315937,"journal":{"name":"Journal of Stock & Forex Trading","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Stock & Forex Trading","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4172/2168-9458.1000E115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
宏观经济的波动性
版权所有:©2012 Rahman S.这是一篇在知识共享署名许可下发布的开放获取文章,该许可允许在任何媒体上不受限制地使用、分发和复制,前提是要注明原作者和来源。波动性在金融文献中获得了大量关注,例如,在股票市场回报与风险之间关系的研究中,但对于应用宏观经济学家来说,它的优先级要低得多。一般来说,现有的宏观计量经济学研究主要关注变量的第一阶矩(或平均值),而系统地忽略了变量的第二阶矩(或方差)。然而,正如Hamilton和Herrera[1]所解释的那样,正确的方差说明仍然很重要,原因有二。首先,错定方差下的假设检验无效。其次,通过将观察到的异方差特征纳入估计过程,可以提高条件均值估计的效率。
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