On the Efficiency Between Forecasting Models—A Study in the Indian Context

1987 ◽  
Vol 36 (1-2) ◽  
pp. 19-28
Author(s):  
Lakshmikanta Datta
Keyword(s):  
Transfer Function ◽  
Moving Average ◽  
Arima Model ◽  
Consumer Price Index ◽  
Transfer Function Model ◽  
Industrial Workers ◽  
Economic Time Series ◽  
Function Model ◽  
Forecasting Models ◽  
Economic Time

In this paper, we have investigated the relative performances of two types of forecasting models, namely univariate autoregressive integrated moving average (ARIMA) model and transfer function model, with the help of two Indian economic time series viz. (i) Money Supply (M3 ) and (ii) Consumer Price Index Numbers for Industrial Workers. Our emperical results show that the efficiency of transfer function model is substantially superior to that of the univariate model.

scholarly journals PERAMALAN JUMLAH KUNJUNGAN WISATAWAN MANCANEGARA YANG BEKUNJUNG KE BALI MENGGUNAKAN FUNGSI TRANSFER

2016 ◽  
Vol 5 (4) ◽  
pp. 139
Author(s):  
I KETUT PUTRA ADNYANA ◽  
I WAYAN SUMARJAYA ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Exchange Rate ◽  
Transfer Function ◽  
Multivariate Time Series ◽  
Arima Model ◽  
Information Criterion ◽  
Percentage Error ◽  
Transfer Function Model ◽  
Function Model ◽  
Diagnostic Checking ◽  
The Exchange Rate

The aim of this research is to model and forecast the number of tourist arrivals to Bali using transfer function model based on exchange rate USD to IDR from January 2009 to December 2015. Transfer function model is a multivariate time series model which can be used to identify the effect of the exchange rate to the number of tourist arrivals to Bali. The first stage in transfer function modeling is identification of ARIMA model in exchange rate USD to IDR variable. The best ARIMA model is chosen based on the smallest Akaike information criterion (AIC). The next stage are as follows identification of transfer function model, estimation of transfer function model, and diagnostic checking for transfer function model. The estimated transfer function model suggests that the number of tourist arrivals to Bali is affected by the exchange rate of the previous eight months. The mean absolute percentage error (MAPE) is equal of the forecasting model to 9,62%.

scholarly journals Peramalan Kebutuhan Daya Listrik Menggunakan Model ARIMA dan Fungsi Transfer (Studi Kasus: PT. PLN (Persero) Area Sumbawa)

2018 ◽  
Vol 4 (2) ◽  
pp. 122-127
Author(s):  
Mikhratunnisa Mikhratunnisa ◽  
Tri Susilawati
Keyword(s):  
Economic Development ◽  
Transfer Function ◽  
Arima Model ◽  
Transfer Function Model ◽  
Customer Demand ◽  
Function Model ◽  
Electrical Capacity ◽  
The Right ◽  
Function Models ◽  
Transfer Function Models

Energy is one of the basic need of human being. One of the vital energy is electricity. The need of electricity in NTB is increase along with the citizen economic development in NTB especially in Sumbawa regency. Therefore, there is a need for the right way in adjusting the amount of electrical capacity to match customer demand. One way that can be done is to forecast/ predict the need for electricity. The forecast can be used by using the ARIMA and Transfer Function models. The results of the study show that using the ARIMA model is estimated to require electricity in 2018 experienced an increase of 18,21% from the previous year, while using the transfer function model is estimated to increase by 18,18% from the previous year.

scholarly journals Prediction Model of Firefighting Supplies Market

2021 ◽  
Vol 35 (4) ◽  
pp. 91-96
Author(s):  
Inseon Park ◽  
Muheon Jeong
Keyword(s):  
Transfer Function ◽  
Construction Industry ◽  
Structural Characteristics ◽  
Arima Model ◽  
Statistical Prediction ◽  
Transfer Function Model ◽  
Short Term ◽  
Function Model ◽  
Construction Market ◽  
The Relationship

The market size of the firefighting supplies industry is can be considered a derivative demand of the construction industry, and its patterns differ seasonally. In this study, the seasonal ARIMA model and the transfer function model were established, and their predictive performances were verified. It was statistically confirmed that the firefighting supplies market has a lagging character in the construction industry. Although the seasonal ARIMA model, a representative time-series model, is a suitable predictive model, the transfer function model linked to the construction market is more useful in the short-term forecast period. This study is the first statistical prediction study of the firefighting supplies market in Korea. Based on the structural characteristics of the firefighting supplies industry, the relationship between the building permit and firefighting supplies production should be established using the transfer function model, the transfer function model is more useful in the decision-making process of firefighting supplies policies and the manufacturer’s marketing.

scholarly journals Analisis Pengaruh Kurs USD terhadap Jakarta Islamic Index dengan Menggunakan Model Fungsi Transfer

2018 ◽  
Vol 2 (2) ◽  
pp. 66-72
Author(s):  
Pika Silvianti ◽  
Nur Laela Fitriani
Keyword(s):  
Exchange Rate ◽  
Transfer Function ◽  
Explanatory Variable ◽  
Arima Model ◽  
Diagnostic Testing ◽  
Forecasting Model ◽  
Transfer Function Model ◽  
Function Model ◽  
Model Diagnostic ◽  
The Exchange Rate

The transfer function model is a time series forecasting model that combines several characteristics ofthe ARIMA model one variable with several characteristics of regression analysis. This model is used to determine the effect of an explanatory variable (input series) on the response variable (output series). This study uses a transfer function model to analyze the effect of the exchange rate on Jakarta Islamic Index. The transfer function model is structured through several stages, starting from modelidentification, estimation of the transfer function model, and model diagnostic testing. Based on the transfer function model, Jakarta Islamic Index was influenced by Jakarta Islamic Index in one and two days earlier and the exchange rate in the same period and one to two days earlier. The forecasting MAPE value of 0.6529% shows that the transfer function model obtained is good enough in forecasting.

scholarly journals PERAMALAN DATA INDEKS HARGA KONSUMEN KOTA PURWOKERTO MENGGUNAKAN MODEL FUNGSI TRANSFER MULTI INPUT

2020 ◽  
Vol 9 (4) ◽  
pp. 515-524
Author(s):  
Inarotul Amani Rizki Ananda ◽  
Tarno Tarno ◽  
Sudarno Sudarno
Keyword(s):  
Transfer Function ◽  
Function Method ◽  
Price Index ◽  
Consumer Price Index ◽  
Health Sector ◽  
Information Criterion ◽  
Transfer Function Model ◽  
Function Model ◽  
Transfer Function Method ◽  
Index Value

The Consumer Price Index (CPI) provides information on changes in the average price of a group of fixed goods or services that are generally consumed by households within a certain period of time. The General CPI is formed from 7 sectors of public consumption expenditure groups. Because the formation of the consumer price index value is influenced by several sectors, the method that can be used is the transfer function method. The purpose of this study is to analyze the transfer function model so that the best model is produced to predict CPI in Purwokerto for the next several periods. In this study, general CPI modeling will be carried out based on the CPI value for the transportation services sector and the CPI for the Health sector in Purwokerto from January 2014 to July 2019 using the multi-input transfer function method. Based on the analysis, the best models are obtained, namely the multi-input transfer function model (2,0,0) (0,1,0) and the ARIMA noise series ([3], 0,0). The model has an Akaike's Information Criterion (AIC) value of 72.42021 and an sMAPE value of  2,351591 % which indicates that the model can be used for forecasting..Keywords: Consumer Price Index (CPI), Inflation,transfer function, AIC

scholarly journals PERBANDINGAN ANTARA MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN MODEL FUNGSI TRANSFER PADA PERAMALAN CURAH HUJAN DI KABUPATEN MANOKWARI

2019 ◽  
Vol 15 (2) ◽  
pp. 78-87
Author(s):  
Esther Ria Matulessy
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Transfer Function Model ◽  
Function Model ◽  
Autoregressive Integrated Moving Average ◽  
West Papua ◽  
Input Variables

This study discusses the comparison of forecasting time series data between the Autoregressive Integrated Moving Average (ARIMA) method and the multi input transfer function model. ARIMA method is one of the most frequently used methods for forecasting time series data. Meanwhile, the transfer function model is a combination of the characteristics of multiple regression analysis with the characteristics of the ARIMA time series. Meanwhile, the multi input transfer function model is a transfer function model that has input variables of more than two time series. The application of these two methods is carried out on rainfall data from January 2012 to December 2017 in Manokwari Regency, West Papua Province. The input variables used are temperature, humidity, solar radiation, air pressure, and wind speed variables. The results showed the best ARIMA model was ARIMA (1,0,0) (2,0,0) 12 with an AIC value of 910.07, while for the best multi input transfer function model was ARIMA (1,1,0) AIC value of 898.24. Between the two methods, the best model used to forecast rainfall in Manokwari Regency, West Papua Province is the multi-input transfer function model (1,1,0).

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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