scholarly journals An Analysis on Forecasting Inflation Rate in the Philippines: A Recurrent Neural Network Method Approach

2021 ◽  
Vol 12 (3) ◽  
pp. 5297-5310
Author(s):  
Jackie D. Urrutia Et. al.
Keyword(s):  
Neural Network ◽  
Monetary Policy ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Money Supply ◽  
Recurrent Neural Network ◽  
Inflation Rate ◽  
The Philippines ◽  
Forecast Efficiency ◽  
Forecasting Inflation

Inflation rate is the proceeding rise within the common level of costs of products and services in an economy over a certain span of time. In 2018, the Philippines has the highest inflation rate among the 10 South East Asian countries. The objective of this research is to forecast the inflation rate of the Philippines for the next five years (2019-2023). Also, the researchers compared the results obtained from the Multiple Linear Regression and Recurrent Neural Network (RNN) performed in MATLAB to determine which of these two models will be the better model in forecasting inflation rate. In this study, the researchers observed the behavior of the Inflation Rate(y) and its economic factors such as Import (x1), Export (x2), Money Supply (x3), Gross Domestic Product (x4), Gross National Product (x5), Expenditure (x6) and Exchange Rate (x7). Using Multiple Linear Regression, this study determined that the significant predictors are Money Supply (x3) and Expenditures (x6). By evaluating the forecast efficiency of the two methods, the researchers concluded that Multilayered Recurrent Neural Network outperforms Multiple Linear Regression in predicting inflation rate of the Philippines. This paper can be useful to the Philippine Government on their decisions about monetary policy making since forecasting the inflation rate has a huge importance and impact in conducting monetary policy.

scholarly journals An Analytical Study On Forecasting Exchange Rate In The Philippines Using Multi-Layer Feed Forward Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 5357-5377
Author(s):  
Jackie D. Urrutia Et. al.
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Price Index ◽  
The Philippines ◽  
Balance Of Payments ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Independent Variables

Exchange Rate is one of the economic indicators in the Philippines. It is the value of the nation’s currency versus the  currency of another country or economic zone. This study aims to forecast the monthly Exchange Rate (y) of the Philippines from November 2018 to December 2023 using Multiple Linear Regression and Multi-Layer Feed Forward Neural Network. The researchers investigate the behaviour of each independent variables – Inflation Rate (x1), Balance of Payments (x2), Interest Rate (x3), Producer’s Price Index (x4), Export (x5), Import (x6), Money Supply (x7), and Consumer’s Price Index (x8) from Philippine Statistics Authority (PSA) starts from January 2007 up to October 2018. Multiple Linear Regression (MLR) was used to identify significant predictors among these independent variables. The Exchange Rate (y) had undergone first difference transformation. Upon running the regression analysis, it has concluded that only two independent variables are significant predictors, namely: Balance of Payments (x2) and Import (x6). Through these significant predictors, the MLR model was formulated. On the other hand, Multi-Layer Feed forward Neural Network (MFFNN) was also used to determine the forecasted values of Exchange Rate (y) for the next five years (2018-2023) given the said independent variables and obtained a model. The researchers compared the model of Multiple Linear Regression and Multi-Layer Feed Forward Neural Network by evaluating the forecasting accuracy of each method.It was concluded that Multi-Layer Feed forward Neural Network is the best fitting model for forecasting the Exchange rate (y) in the Philippines. This paper will serve as a tool of awareness for the government to forsee the trend of Exchange Rate in the Philippines on the next five years  (2018-2023) for Monetary Policy making and to prevent the possible depreciation of peso vs. dollar.

scholarly journals MENINJAU KEMBALI SEBERAPA PENTING TARGET INFLASI BANK INDONESIA DALAM MENGONTROL LAJU INFLASI

2017 ◽  
Vol 20 (1) ◽  
pp. 53 ◽  
Author(s):  
Rachman Hakim
Keyword(s):  
Monetary Policy ◽  
Linear Regression ◽  
Gross Domestic Product ◽  
Multiple Linear Regression ◽  
Inflation Targeting ◽  
Inflation Rate ◽  
Inflation Expectations ◽  
Crucial Issue ◽  
Inflation Target ◽  
Policy Actors

Inflation is a crucial issue for a development country such as Indonesia. To solve the problem of inflation, Bank Indonesia as the monetary policy actors trying to adopt inflation targeting system. Every year Bank Indonesia announced its inflation target with the goal of keeping actual inflation will also lead there. However, the results obtained are less appropriate expectations for Bank Indonesia's inflation target is often off the mark. It is interesting to discuss the actual extent of the inflation target can affect the rate of inflation. Many disagreements related to it. This study wanted to reveal how the influence of the inflation target to actual inflation rate, especially in Indonesia. The method used is multiple linear regression. In addition to the inflation target, there are other variables to be studied its effect on the rate of actual inflation, ie inflation earlier period, inflation expectations and the Gross Domestic Product (GDP). The results showed that previous periods of inflation, inflation expectations and GDP significantly influence the rate of inflation. In contrast, Bank Indonesia's inflation target does not significantly influence the rate of inflation in Indonesia. This can happen due to the lack of credibility of Bank Indonesia in front of Indonesian, especially in the application of inflation targeting.

scholarly journals ANALISIS PENGARUH JUMLAH UANG BEREDAR, SUKU BUNGA SBI, NILAI TUKAR TERHADAP TINGKAT INFLASI

Media Ekonomi ◽  
2017 ◽  
Vol 18 (2) ◽  
pp. 49
Author(s):  
Heru Perlambang
Keyword(s):  
Monetary Policy ◽  
Exchange Rate ◽  
Interest Rate ◽  
Linear Regression ◽  
Exchange Rates ◽  
Interest Rates ◽  
Money Supply ◽  
Inflation Rate ◽  
Test Results ◽  
The Interest Rate

<p>Inflation is one of the effects of a prolonged economic crisis that hit the<br />country. Inflation is a situation where there are price rises sharply (Absolute)<br />which continues over a period of time. The purpose of this study analyzes the<br />monetary policy conducted by Bank Indonesia and its influence as the money<br />supply, interest rates and exchange rates SBI (IDR / USD) of the inflation rate.<br />The method used is multiple linear regression based on test results indicate<br />avariable effect on money supply, interest rate of SBI, and the exchange rate<br />(Rp / USD) in 2004 to 2009. By using eviews 4.0 software obtained from the<br />results of research following the money supply and exchange rate (Rp/USD)<br />had no significant effect on inflation while the interest rate (SBI) have a<br />significant effect on inflation.</p>

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

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