Consistency for the least squares estimator in a transfer function model

1984 ◽  
Vol 21 (01) ◽  
pp. 88-97
Author(s):  
Victor Solo
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Least Squares ◽  
Time Series Model ◽  
Least Squares Estimator ◽  
Transfer Function Model ◽  
Function Model ◽  
Mild Conditions ◽  
Function Time

The consistency is developed under mild conditions for the least squares estimator of the parameters of a transfer function time series model.

Consistency for the least squares estimator in a transfer function model

1984 ◽  
Vol 21 (1) ◽  
pp. 88-97
Author(s):  
Victor Solo
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Least Squares ◽  
Time Series Model ◽  
Least Squares Estimator ◽  
Transfer Function Model ◽  
Function Model ◽  
Mild Conditions ◽  
Function Time

The consistency is developed under mild conditions for the least squares estimator of the parameters of a transfer function time series model.

scholarly journals Modelling of Multi Input Transfer Function for Rainfall Forecasting in Batu City

CAUCHY ◽  
2017 ◽  
Vol 5 (1) ◽  
pp. 29
Author(s):  
Priska Arindya Purnama
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Transfer Function ◽  
Air Temperature ◽  
Multivariate Time Series ◽  
Time Series Model ◽  
Transfer Function Model ◽  
Air Humidity ◽  
Rainfall Forecasting ◽  
Function Model

The aim of this research is to model and forecast the rainfall in Batu City using multi input transfer function model based on air temperature, humidity, wind speed and cloud. Transfer function model is a multivariate time series model which consists of an output series (Y<sub>t</sub>) sequence expected to be effected by an input series (X<sub>t</sub>) and other inputs in a group called a noise series (N<sub>t</sub>). Multi input transfer function model obtained is (<em>b<sub>1</sub>,s<sub>1</sub>,r<sub>1</sub></em>) (<em>b<sub>2</sub>,s<sub>2</sub>,r<sub>2</sub></em>) (<em>b<sub>3</sub>,s<sub>3</sub>,r<sub>3</sub></em>) (<em>b<sub>4</sub>,s<sub>4</sub>,r<sub>4</sub></em>)(<em>p<sub>n</sub>,q<sub>n</sub></em>) = (0,0,0) (23,0,0) (1,2,0) (0,0,0) ([5,8],2) and shows that air temperature on t-day affects rainfall on t-day, rainfall on t-day is influenced by air humidity in the previous 23 days, rainfall on t-day is affected by wind speed in the previous day , and rainfall on day t is affected by clouds on day t. The results of rainfall forecasting in Batu City with multi input transfer function model can be said to be accurate, because it produces relatively small RMSE value. The value of RMSE data forecasting training is 7.7921 while forecasting data testing is 4.2184. Multi-input transfer function model is suitable for rainfall in Batu City.

Over-Determined Estimation of Pulse Transfer Function Model by Least-Squares Method

1991 ◽  
Vol 24 (3) ◽  
pp. 823-828
Author(s):  
M. Eguchi ◽  
K. Wada
Keyword(s):  
Transfer Function ◽  
Least Squares ◽  
Least Squares Method ◽  
Transfer Function Model ◽  
Function Model

Iterative Least Squares Estimation and Identification of the Transfer Function Model

1997 ◽  
Vol 18 (6) ◽  
pp. 579-592 ◽  
Author(s):  
Daniel Muller ◽  
William W. S. Wei
Keyword(s):  
Transfer Function ◽  
Least Squares ◽  
Least Squares Estimation ◽  
Transfer Function Model ◽  
Function Model

scholarly journals PERAMALAN JUMLAH KUNJUNGAN WISATAWAN MANCANEGARA DI KEPULAUAN RIAU DENGAN MENGGUNAKAN MODEL FUNGSI TRANSFER

2020 ◽  
Vol 9 (2) ◽  
pp. 152-161
Author(s):  
Tamura Rolasnirohatta Siahaan ◽  
Rukun Santoso ◽  
Alan Prahutama
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Transfer Function Model ◽  
Analysis Model ◽  
Function Model ◽  
The Past ◽  
Causal Approach ◽  
Function Models ◽  
Related Time Series ◽  
Transfer Function Models

Transfer function models is a data analysis model that combines time series and causal approach, in another words, transfer function models is a method that ilustrates that the predicted value in teh future is affected by the past value time series and based on one or more related time series. In this research, an analysis of the number of tourist arrival and rainfall in several regions in Kepulauan Riau from January 2013 until December 2017 was aimed at obtaining a transfer function model and forecasting the number of tourist arrival in several regions of the Kepulauan Riau for next periods. Based on the result of the analysis, rainfall in Tanjung Pinang does not affect the visit of tourist with the values of MAPE is 13,63494%. Rainfall in Batam also does not affect the visit of tourist with the values of MAPE is 7,977151%. While in Tanjung Balai Karimun, tourist arrivals was affected by rainfall with the values of MAPE is 10,32777%.

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).

scholarly journals PERAMALAN JUMLAH PENDERITA DEMAMBER DARAH DENGUE DI KABUPATEN JOMBANG JAWA TIMUR DENGAN PENDEKATAN FUNGSI TRANSFER SINGLE INPUT

2018 ◽  
Vol 15 (2) ◽  
pp. 10
Author(s):  
Sediono Sediono
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Time Series Analysis ◽  
Dengue Fever ◽  
Stationary Time Series ◽  
Transfer Function Model ◽  
Function Model ◽  
Series Analysis ◽  
Future Data ◽  
Single Input

AbstractForecasting  is an important things in time series analysis, because by obtaining a convenient model that is statictically appropriate. Clearly, that can be used to predict the structure of future data form. Transfer function is one of mathematical model  in time series analysis, that can be used to forecasting time index data both univariate and multivariate. Transfer function describes the predictive value  of the output series (Yt) based on the value of one or more input series(Xt). The single input transfer function model is a transfer function model that uses one variable as input series (Xt), where each series of both input series and output series must be a stationary time series model, both stationary in the mean and stationary in variant. One of the used transfer function is to govern a model and forecasting of the number of cases dengue fever (Yt) in Kabupaten Jombang, East Java, where the input variable based on data of rainfall (Xt). From the result of this study was obtained that model of transfer function has a equation Y𝑡 = 0,0542X𝑡+  (1 − 0,7309𝐵)(1 + 0,6568𝐵12) with parameter ωo = 0.0542, ∅1 = 0.7309 and  Φ12 = -0.6568. From the model, it can be interpreted that the number of dengue sufferers for a particular month was influenced by the rainfall on those month and the months before. According to the model of the transfer function, it can be used to forecast the number of sufferers of dengue fever in Kabupaten Jombang  for period next 20 months. After compared between data of forecasting and actual data, there exists equally  trend, namely 15 months of 20 month that are forecasted, such that  it can be explain that majority 75% of the results of forecasting in this study are valid. Keywords: forecasting , single input transfer function, stationer point, Dengue fever  Abstrak Peramalan adalah sesuatu hal yang penting dalam analisis runtun waktu, karena dengan diperolehnya sebuah model yang tepat secara statistik, jelas hal tersebut dapat digunakan untuk memprediksi struktur pola data yang akan datang. Fungsi transfer merupakan salah satu model matematis dalam analisis runtun waktu  yang dapat digunakan untuk  peramalan data indekswaktu baik univariat maupun multivariat. Fungsi transfer menggambarkan nilai prediksi  dari  output series (Yt) berdasarkan nilai satu atau lebih input series (Xt). Model fungsi transfer  single input  adalah model fungsi transfer yang menggunakan satu variabel sebagai input series (Xt), dimana masing-masing series baik input series maupun output series keduanya harus sama-sama merupakan model runtun waktu yang stasioner, baik stasioner dalam mean maupun stasioner dalam varian. Salah satu penggunaan  model fungsi transfer ini adalah untuk pembuatan model dan peramalan jumlah kasus demam berdarah dengue (Yt)  di Kabupaten Jombang Jawa Timur, dengan variabel inputnya berdasarkan data curah hujan (Xt). Dari hasil penelitian diperoleh  model fungsi transfer yang memiliki persamaan  Y𝑡 = 0,0542X𝑡 +  (1 − 0,7309𝐵)(1 + 0,6568𝐵12) 𝑎𝑡 , dengan parameter ωo = 0,0542, ∅1 = 0,7309, dan Φ12 = -0,6568. Dari model tersebut dapat diinterpretasikan bahwa jumlah penderita demam berdarah dengue pada suatu bulan dipengaruhi curah hujan pada bulan itu, dan dipengaruhi oleh beberapa gangguan pada bulan-bulan sebelumnya. Selanjutnya berdasarkan model fungsi transfer tersebut dapat digunakan untuk peramalan jumlah penderita demam berdarah dengue di Kabupaten Jombang untuk periode 20 bulan kedepan. Setelah dilakukan perbandingan antara data hasil peramalan dengan data aktual, terdapat kesamaan trend yaitu sejumlah 15 bulan dari 20 bulan yang diramalkan, sehingga dapat dijelaskan bahwa sebagian besar yaitu 75% dari hasil peramalan  dalam penelitian ini adalah valid. Kata Kunci : Peramalan, Fungsi transfer single input, stasioner, Demam Berdarah Dengue. 

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.

scholarly journals Analysis of Morton Effect Induced Vibration based on Transfer Function Model: Influence of L/D ratio of Journal Bearing and Rotational speed

2021 ◽  
Vol 1909 (1) ◽  
pp. 012079
Author(s):  
Takafumi Suzuki ◽  
Jotaro Chiba ◽  
Shota Yabui ◽  
Shigeyuki Tomimatsu ◽  
Tsuyoshi Inoue
Keyword(s):  
Transfer Function ◽  
Rotational Speed ◽  
Journal Bearing ◽  
Transfer Function Model ◽  
Function Model ◽  
Morton Effect
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