This study aims to approach the Fisher effect issue from a different methodological perspective. To this aim, the nonlinear autoregressive distributed lag (ARDL) model, recently developed by Shin et al. (2014), is applied for South Korea between 2000Q4–2017Q4. This model allows us to decompose one variable (changes in inflation) into two new variables (increases and decreases in inflation) under the manners of nonlinearity and asymmetry. Hence, it enables us to monitor the Fisher effect in terms of increases and decreases separately. We also apply the linear version of the same model since the nonlinear ARDL model is the extended version of linear ARDL model. While the empirical findings of the nonlinear model support asymmetrically partial Fisher effects in the long-run for 1, 3, 5 and 10-years Korean bond rates, the linear model does not. Additionally, the nonlinear model detects lower size partial Fisher effects when the maturity of interest rates gets longer. Another finding of this study is that the nonlinear model may mathematically identify and introduce a different version of the partial Fisher effect based on singular (separate) effects of each decomposed variable in a parametric manner.

中文翻译：

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在非线性和线性ARDL模型的应用中重新考虑韩国的Fisher方程

这项研究旨在从不同的方法论角度探讨费雪效应问题。为此，Shin等人最近开发了非线性自回归分布滞后（ARDL）模型。（2014），适用于2000年第4季度至2017年第4季度之间的韩国。该模型允许我们以非线性和不对称的方式将一个变量（通货膨胀的变化）分解为两个新变量（通货膨胀的增加和减少）。因此，它使我们能够分别根据增加和减少来监视Fisher效应。由于非线性ARDL模型是线性ARDL模型的扩展版本，因此我们也应用了同一模型的线性版本。虽然非线性模型的经验发现长期支持韩国1、2、3、5和10年韩国债券利率的不对称部分费雪效应，但线性模型却不支持。此外，当利率到期日较长时，非线性模型会检测到较小尺寸的部分费舍尔效应。这项研究的另一个发现是，非线性模型可以基于每个分解变量的奇异（分离）效应，以参数方式在数学上识别并引入部分Fisher效应的不同版本。