Forecasting the Monthly Rediscounting Rate in the Philippines

摘要

Objective: To develop a best model that can forecast the monthly rediscounting rate in the Philippines. Methods/Statistical Analysis: The researcher utilized the numbers from the certified metadata of the BankoSentral ng Pilipinas (BSP) from January 2009 to December 2015 to shape a forecasting model using the modern forecasting method under time series analysis specifically the Box-Jenkins Procedure. For accurateness of calculations, the researcher employs the use of Statistical Software. Furthermore, the researcher sought to find the finest model using the said forecasting scheme from January 2015 to December 2018. Findings: The results show that the series is persistent at a 4% rate from February 2010 to February 2011 while it slightly reduced over eight more months. However, another constancy in the monthly Philippine Rediscounting rate happened from November 2012 to October 2013 having a rate of 3.5%. Moreover, after such constancy the monthly Philippine rediscounting rate continued to increase until December 2015. After the testing the assumptions and diagnostic test of model adequacy, the researcher finally creates a best model that can predict the future values of rediscount rate in the Philippines based on the evaluation criteria in selecting the best model. The “best” model for the monthly Philippine rediscounting rate in terms of its significant factors IR (Inflation Rate), MMR (Money-Market Rate), TBR (Treasury Bill Rate) and PDR (Peso-Dollar Rate) is Monthly Rediscounting Rate (Predicted) = 0.096(IR) – 0.186(MMR) + 0.310(TBR) + 0.09(PDR). This model building can help the government to create a strategy or system to maintain price stability in the country and help the countrymen to access the basic needs for their everyday life and can be applied to pact with balance-of-payments deficits so as to regulate international movements of capital. Applications/Improvements: Outcome from this study can be used as a monetary tool of the BSP to regulate the level of liquidity in the system. Adding observations from the previous series of the monthly Philippine rediscounting rate may help in discovering a better model in forecasting.