scholarly journals Forecasting of fuel oil supply using the transfer function approach (Case study: PT. Agrabudi Karyamarga Gas Station Division 64.706.07)

2021 ◽  
Vol 2106 (1) ◽  
pp. 012007
Author(s):  
A U Labibah ◽  
Y Sukmawaty ◽  
D S Susanti
Keyword(s):  
Transfer Function ◽  
Historical Data ◽  
Fuel Oil ◽  
Function Approach ◽  
Transfer Function Model ◽  
Function Model ◽  
Several Variables ◽  
Forecasting Method ◽  
Forecast Data

Abstract In everyday life, fuel oil is quite important. The need for fuel is increasing every day, which means that the supply of fuel oil must keep up with the demand. As a reason, we require a way for predicting future fuel needs. The forecasting method is one that is frequently utilized. Forecasting is a method for predicting future conditions based on historical data. The transfer function approach is one way to forecast data with several variables in time series analysis. The objective of this research is to estimate the parameters of the transfer function model and use a transfer function approach to predict the movement of fuel, particularly pertalite. The parameter estimation results in this research are ω ^ 0 = 0.033 ; ω ^ 1 = − 0.0358 ; ω ^ 2 = 0.0627 ; δ ^ 1 = − 0.9713 ; δ ^ 2 = 1 ; θ ^ 1 = − 0.9141 , and the forecast value for the 214th period is 8762.61, based on the data used, namely for 213 periods starting from the 1st period until the 213th period.

Model Fungsi Transfer Input Ganda untuk Pemodelan Jakarta Islamic Index

2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Nur Laela Fitriani ◽  
Pika Silvianti ◽  
Rahma Anisa
Keyword(s):  
Exchange Rate ◽  
Transfer Function ◽  
Multivariate Time Series ◽  
Model Identification ◽  
Transfer Function Model ◽  
Function Model ◽  
Jones Index ◽  
Multiple Input ◽  
Identification Stage ◽  
Stage Estimation

Transfer function model with multiple input is a multivariate time series forecasting model that combines several characteristics of ARIMA models by utilizing some regression analysis properties. This model is used to determine the effect of output series towards input series so that the model can be used to analyze the factors that affect the Jakarta Islamic Index (JII). The USD exchange rate against rupiah and Dow Jones Index (DJI) were used as input series. The transfer function model was constructed through several stages: model identification stage, estimation of transfer function model, and model diagnostic test. Based on the transfer function model, the JII was influenced by JII at the period of one and two days before. JII was also affected by the USD exchange rate against rupiah at the same period and at one and two days before. In addition, the JII was influenced by DJI at the same period and also at period of one until five days ago. The Mean Absolute Prencentage Error (MAPE) value of forecasting result was 0.70% and the correlation between actual and forecast data was 0.77. This shows that the model was well performed for forecasting JII.

scholarly journals Administrative Costs and Tariff Rates in the Presence of Customs Evasion: Evidence from Ecuador

Economies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 21
Author(s):  
Jazmín González Aguirre ◽  
Alberto Del Villar
Keyword(s):  
International Trade ◽  
Transfer Function ◽  
Gross Domestic Product ◽  
Foreign Trade ◽  
Transfer Function Model ◽  
Administrative Cost ◽  
Function Model ◽  
Administrative Costs ◽  
Tariff Rates ◽  
Budgetary Expenditure

This paper seeks to assess the effectiveness of customs policies in increasing the resources devoted to controlling and inspection. Specifically, it seeks to analyze whether an increase in the administrative cost of collecting taxes on foreign trade in Ecuador contributes to reducing customs fraud. To this end, we identify and estimate a transfer function model (ARIMAX), considering information on foreign trade such as official international trade statistics report and tariff rates, as well as the execution of budgetary expenditure and Ecuador’s gross domestic product (GDP). The period under study includes quarterly series from 2006 to 2018. The results obtained by the model indicate that allocating greater material and budgetary resources to combat customs fraud does not always achieve the objective of reducing customs evasion.

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