关于美元对-à-vis印度卢比名义汇率确定的调查

Punit Pillin
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引用次数: 1

摘要

在本文中,我在一组经济理论的帮助下,研究了在VEC、VAR(在第一差分中)和贝叶斯VAR规范下确定美元对-à-vis In - R名义汇率的样本外预测性能。然后将这些模型的预测性能与随机游走模型进行比较,并将随机游走模型作为预测评价的基准模型。研究发现,VEC规范下的结构模型的预测能力优于BVAR和DVAR。u统计量表明,收益率曲线模型在一年以内具有最小的预测误差。此外,还观察到收益率曲线,裁缝规则基本和增强粘性价格微观结构模型通过VEC规范产生了与长达3年的更长时间内汇率运动的可能行为有关的重要见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey on the Determination of Nominal Exchange Rate for USD vis-à-vis INR
In this paper, I have investigated the out of sample forecast performance for a case study on the determination of the nominal exchange rate for USD vis-à-vis IN¬R under VEC, VAR (in first difference) and Bayesian VAR specification with the help of set of economic theories. The forecast performance of these models is then compared with the random walk model, which is set as a benchmark model for forecast evaluation. The study observed that the structural model under VEC specification have superior predictive ability over BVAR and DVAR. U-statistics suggests that the yield curve model has minimum forecast error up to one year. Moreover, it is also observed that the yield curve, the tailor rule fundamental and augmented sticky price – microstructure model produces significant insight pertaining to the likely behavior in the movement of the exchange rate for a longer horizon up to 3 years through VEC specification.
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