A Production Function Model for Aggregate Time‐Series Data

1975 ◽  
Vol 57 (1) ◽  
pp. 122-123
Author(s):  
C. Robert Taylor
Keyword(s):  
Time Series ◽  
Production Function ◽  
Time Series Data ◽  
Series Data ◽  
Function Model

scholarly journals Energy Demand Relationship: Theory and Empirical Application. A Short Note

2020 ◽  
Author(s):  
Fakhri J. Hasanov ◽  
Jeyhun L. Mikayilov
Keyword(s):  
Time Series ◽  
Production Function ◽  
Energy Demand ◽  
Demand Function ◽  
Time Series Data ◽  
Short Note ◽  
Data Driven ◽  
Series Data ◽  
Industrial Energy ◽  
Empirical Analyses

In this short note, the described step-by-step derivations of the industrial energy demand function from the production function framework and provided researchers with two specifications. Then we applied these theoretical specifications to the time series data as empirical analysis. We concluded that theories should be considered at the beginning of the empirical analyses but the data also should be allowed to speak freely. Hence, the main suggestion of this short note is that it would be a better strategy to consider the combination of theory-driven and data-driven approaches in the empirical analyses.

scholarly journals A Non-linear Function-on-Function Model for Regression with Time Series Data

2020 ◽  
Author(s):  
Qiyao Wang ◽  
Haiyan Wang ◽  
Chetan Gupta ◽  
Aniruddha Rajendra Rao ◽  
Hamed Khorasgani
Keyword(s):  
Time Series ◽  
Linear Function ◽  
Time Series Data ◽  
Series Data ◽  
Function Model ◽  
Non Linear

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).

تقدير وتحليل إنتاجية البنوك التجارية في اليمن: دراسة قياسية

2021 ◽  
Vol 2021 (71) ◽  
pp. 147-163
Author(s):  
أد. مشارك غالب صالح مشبب ◽  
◽  
أ.د مشارك عيسى احمد حنوم
Keyword(s):  
Time Series ◽  
Production Process ◽  
Production Function ◽  
Commercial Banks ◽  
Time Series Data ◽  
The Other ◽  
Production Functions ◽  
Series Data ◽  
Cross Sectional ◽  
The One

This study aimed to estimate and analyze the productivity of a sample of commercial banks in Yemen, and the technical relationship and its substitution between the inputs on the one hand, and between them and the outputs on the other hand, with an indication of the efficiency of these banks, where the focus was placed on the two production functions with fixed substitution elasticity by approximating a potential, and a function Cobb-Duclas production based on balanced cross-sectional time-series data during the period (2000-2014); The number of views of the study was 90 views. The results of the study showed that the production function (Cobb-Duclas) is the most appropriate in estimating and explaining the behavior of the production process of the commercial banks in the study sample. and that the productive share of the capital component is greater than that of the labor component. Finally, the results showed that it is possible to replace and exchange between production elements.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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