Analyzing The Effect of BI 7-Days Repo Rate on The Jakarta Composite Index Using Nonparametric Regression Approaches Based on Least Square Spline Estimator

Christopher Andreas; Feevrinna Yohannes  Harianto; Elfhira Juli  Safitri; Nur Chamidah

doi:10.20956/j.v17i3.13101

Authors

Christopher Andreas Universitas Airlangga
Feevrinna Yohannes Harianto
Elfhira Juli Safitri
Nur Chamidah

DOI:

https://doi.org/10.20956/j.v17i3.13101

Keywords:

BI 7-Days Repo Rate, Jakarta Composite Index, Least Square Spline, Nonparametric Regression, Parametric Regression

Abstract

During the Covid-19 pandemic, the Indonesia stock market was under great pressure, so that the value of the Jakarta Composite Index (JCI) fluctuated greatly. To maintain economic stability, Bank Indonesia has regulated monetary policy such as setting the BI 7-Days Repo Rate. Analysis of this effect is important to formulate the right policy. This study aims to design the best model in describing the relationship between JCI value and BI 7-Days Repo Rate. The analysis was carried out by using parametric regression approach based on the ordinary least square method and nonparametric regression approach based on least square spline estimator. The results showed that the parametric regression models failed to meet the classical assumptions. Meanwhile, nonparametric regression can produce an optimal model with high accurate prediction, with an overall mean absolute percentage error value of 3.16%. Furthermore, mean square error, coefficient of determination, and mean absolute deviation also show good results. Thus, the effect of the BI 7-Days Repo Rate on the JCI value forms a quadratic pattern, in which a positive relationship is formed when the BI 7-Days Repo Rate is set at more than 4.25% and vice versa for a negative relationship.

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