印度货币政策评估:SVAR 方法

Bushra Ateeq
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引用次数: 0

摘要

本文以月度为基础,对 2000 年至 2019 年期间印度货币政策传导变量的强度进行了研究。模型中的变量包括国内生产总值、银行利率、批发价格指数和汇率。结构矢量自回归模型(SVAR)的技术已被用于清晰地感知变量之间的同期关系,从而提供模型中每个变量的相对重要性和强度。首先,所有变量都具有统计意义。同时,我们发现,生产价格指数和国内生产总值更为突出,而汇率对银行部门的工作要求发生了关键性的变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Evaluation of Monetary Policy in India: A SVAR Approach
This paper sets an examination for the strength of the monetary policy transmission variables in India for the period 2000 to 2019 on the monthly basis. The variables incorporated in the model are Gross Domestic Product, Bank Rate, Wholesale Price Index and Exchange rate. The techniques of Structural Vector Autoregressive Model (SVAR) have been applied to perceive the clear picture of the contemporaneous relationship among the variables, thus providing with the relative importance and strength of each variable within the model. In the first place, all the variables were instituted to be statistically significant. At the same time, WPI and GDP are found to be more prominent, while exchange rate demanding a pivotal change in the working of the banking sector.
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