A switching regression model with different change-points for individual coefficients and its application to the energy demand equations for Japan

1989 ◽  
Vol 14 (2) ◽  
pp. 93-103 ◽  
Author(s):  
T. Toyoda ◽  
K. Ohtani
Keyword(s):  
Regression Model ◽  
Energy Demand ◽  
Change Points ◽  
Switching Regression ◽  
Demand Equations

scholarly journals Credit Constraint and Rural Household Welfare in the Mezam Division of the North-West Region of Cameroon

2021 ◽  
Vol 13 (11) ◽  
pp. 5964
Author(s):  
Louis Atamja ◽  
Sungjoon Yoo
Keyword(s):  
Regression Model ◽  
Rural Household ◽  
Household Welfare ◽  
Switching Regression ◽  
Probit Regression ◽  
Credit Constraint ◽  
Endogenous Switching Regression ◽  
North West ◽  
The North ◽  
West Region

The purpose of this study is to examine the effect of the rural household’s head and household characteristics on credit accessibility. This study also seeks to investigate how credit constraint affects rural household welfare in the Mezam division of the North-West region of Cameroon. Using data from a household survey questionnaire, we found that 36.88% of the households were credit-constrained, while 63.13% were unconstrained. A probit regression model was used to examine the determinants of households’ credit access, while an endogenous switching regression model was used to analyze the impact of credit constraint on household welfare. The results from the probit regression model indicate the importance of the farmer’s or trader’s organization membership, occupation, and savings to the household’s likelihood of being credit-constrained. On the other hand, a prediction from the endogenous switching regression model confirms that households with access to credit have a better standard of welfare than a constrained household. From the results, it is necessary for the government to subsidize microfinance institutions, so that they can take on the risk of offering credit to rural households.

A Simulation Model for Multi-Factor Sensitivity Analysis on Energy Demand

2012 ◽  
Vol 621 ◽  
pp. 352-355
Author(s):  
Zhong Fu Tan ◽  
Shu Xiang Wang ◽  
Chen Zhang ◽  
Li Qiong Lin ◽  
Yin Hui Zhao
Keyword(s):  
Sensitivity Analysis ◽  
Energy Efficiency ◽  
Regression Model ◽  
Simulation Model ◽  
Influencing Factors ◽  
Energy Demand ◽  
Analysis Model ◽  
Demand Forecast ◽  
Population Urbanization

This paper analyses multi influencing factors of energy demand, using energy demand forecast regression model reveals inner relations between each factor and energy demand. Establish simulation model of the relation between GDP, energy intense and energy demand. Under the change in population, urbanization and energy efficiency, this paper gives analysis model of energy demand change.

scholarly journals Analyzing the Influence of Transportations on Chinese Inbound Tourism: Markov Switching Penalized Regression Approaches

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 515
Author(s):  
Woraphon Yamaka ◽  
Xuefeng Zhang ◽  
Paravee Maneejuk
Keyword(s):  
Structural Changes ◽  
Markov Switching ◽  
Penalized Regression ◽  
Tourism Demand ◽  
Switching Regression ◽  
Inbound Tourism ◽  
Dimensional Parameters ◽  
Demand Equations ◽  
Transportation Development ◽  
Positive Effect

This study investigates the nonlinear impact of various modes of transportation (air, road, railway, and maritime) on the number of foreign visitors to China originating from major source countries. Our nonlinear tourism demand equations are determined through the Markov-switching regression (MSR) model, thereby, capturing the possible structural changes in Chinese tourism demand. Due to many variables and the limitations from the small number of observations confronted in this empirical study, we may face multicollinearity and endogeneity bias. Therefore, we introduce the two penalized maximum likelihoods, namely Ridge and Lasso, to estimate the high dimensional parameters in the MSR model. This investigation found the structural changes in all tourist arrival series with significant coefficient shifts in transportation variables. We observe that the coefficients are relatively more significant in regime 1 (low tourist arrival regime). The coefficients in regime 1 are all positive (except railway length in operation), while the estimated coefficients in regime 2 are positive in fewer numbers and weak. This study shows that, in the process of transportation, development and changing inbound tourism demand from ten countries, some variables with the originally strong positive effect will have a weak positive effect when tourist arrivals are classified in the high tourist arrival regime.

scholarly journals Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3996 ◽  
Author(s):  
Marwen Elkamel ◽  
Lily Schleider ◽  
Eduardo L. Pasiliao ◽  
Ali Diabat ◽  
Qipeng P. Zheng
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Multiple Regression ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Multiple Regression Model ◽  
Electricity Demand ◽  
Degree Days ◽  
Population Number ◽  
Demand Prediction

Predicting future energy demand will allow for better planning and operation of electricity providers. Suppliers will have an idea of what they need to prepare for, thereby preventing over and under-production. This can save money and make the energy industry more efficient. We applied a multiple regression model and three Convolutional Neural Networks (CNNs) in order to predict Florida’s future electricity use. The multiple regression model was a time series model that included all the variables and employed a regression equation. The univariant CNN only accounts for the energy consumption variable. The multichannel network takes into account all the time series variables. The multihead network created a CNN model for each of the variables and then combined them through concatenation. For all of the models, the dataset was split up into training and testing data so the predictions could be compared to the actual values in order to avoid overfitting and to provide an unbiased estimate of model accuracy. Historical data from January 2010 to December 2017 were used. The results for the multiple regression model concluded that the variables month, Cooling Degree Days, Heating Degree Days and GDP were significant in predicting future electricity demand. Other multiple regression models were formulated that utilized other variables that were correlated to the variables in the best-selected model. These variables included: number of visitors to the state, population, number of consumers and number of households. For the CNNs, the univariant predictions had more diverse and higher Root Mean Squared Error (RMSE) values compared to the multichannel and multihead network. The multichannel network performed the best out of the three CNNs. In summary, the multichannel model was found to be the best at predicting future electricity demand out of all the models considered, including the regression model based on the datasets employed.

FUZZY MULTIPLE OBJECTIVE PROGRAMMING IN AN INTERVAL PIECEWISE REGRESSION MODEL

2009 ◽  
Vol 17 (03) ◽  
pp. 365-376 ◽  
Author(s):  
JING-RUNG YU ◽  
GWO-HSHIUNG TZENG
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Heuristic Approach ◽  
Multiple Objective Programming ◽  
Change Points ◽  
Multiple Objective ◽  
Compromise Solution ◽  
Numerical Examples ◽  
Piecewise Regression ◽  
Objective Programming

This study proposes fuzzy multiple objective programming to determine the measure of fitness and the number of change-points in an interval piecewise regression model. To increase the measure of fitness, Tanaka and Lee proposed a conceptual procedure, which is a heuristic approach and becomes complicated for determining the proper polynomial. Therefore, a multiple objective approach is adopted to obtain a compromise solution among three objectives — maximizing the measure of fitness, minimizing the number of change-points and minimizing the width to obtain the interval regression models. By using the proposed method, a better measure of fitness can be obtained. Two numerical examples are used as demonstrations to illustrate our approach in more detail.

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