Informatics Publishing Limited

Jackie D. Urrutia ¹, Joseph Mercado ¹, Katherine Eve E. Ebue ¹, Faila S. Raymundo ¹, Bernadeth G. Nobles ¹

Affiliations
1 Polytechnic University of the Philippines Sta. Mesa, Manila, PH

Abstract

Objectives: This paper aims to identify the factors influencing agricultural productivity which is measured by the real gross value added in agriculture and provide suggestion. Specifically, how Agricultural Land Area, Employment in Agriculture, Government Expenditure on Agriculture, Agriculture Raw Material Export, and Electric Consumption per Capita affects Real Gross Value Added. Taking account, the agricultural production function of the three basic factors of production – land, labor and capital together with the raw material export and electric consumption. Methods/Statistical Analysis: The Philippine Agriculture is at risk causing poverty and employment, as reported year 2013, compared to how it boosts the economy decades ago. Through multiple regression analysis, the relationship between factors Agricultural Land Area, Employment in Agriculture, Government Expenditure on Agriculture, Agriculture Raw Material Export, and Electric Consumption per Capita and Real Gross Value Added has been established. Pearson Correlation test was used to check to what is the relationship between the Agricultural Gross Value Added and the independent variables. Since the data available for land area is in percent of the total land area, the data for Agricultural Land Area were converted into square kilometres to attain best result. The data always went through Test of Individual Significance, Granger-Causality Test and Paired-T Test. Findings: The dependent variables Agricultural Land Area, Employment, Government Expenditure on Agriculture, Agriculture Raw Material Export, Electric Consumption per Capita have p-values of 0.0008, 0.0014, 0.0042, 0.0000, and 0.0002, respectively. Hence, the individual factors stated were found to contribute significantly to the Agriculture Real Gross Value Added. The correlation of coefficient of Agricultural Land Area, Employment, Government Expenditure on Agriculture, and Electric Consumption per Capita show a strong positive correlation to Real Gross Value Added, whilst, the correlation coefficient of Agricultural Raw Material Export implies a strong negative correlation. In line with the existing values about production, an additional unit for inputs would create an additional output. The conversion of agriculture lands into industrial and commercial space, and the likes, greatly affect in the decrease of agricultural production. The more agriculture raw materials Philippines would export, obviously, the lesser production. But, what if Philippines lack the ability to turn those raw materials into a new product? And, it is better to export it to generate an income. Then, it would be great if Philippines will also invest in agriculture raw materials processing. Application/Improvements: Real Gross Value Added contributes to Gross Domestic Product, which is a measurement of how well an economy of a certain country is. In other words, it affects the economy as a whole. Today’s Philippine administration has been promoting boost in agriculture. In line with this paper’s findings, the government pours sufficient financial support in Agriculture sector to address its needs. This paper will be more useful if it the data available would be up-to-date, since, data availability depends on the scheduling of census and funds as well.

Keywords

Agriculture Gross Value Added, Agriculture Production, Macroeconomics, Multiple Linear Regression, Philippine Agriculture, Time Series

Full Text

   

Jackie D. Urrutia ¹, Nathan C. Resurreccion ¹, Leah Mariel C. Visco ¹, Lincoln A. Bautista ¹, Rolan J. Malvar ¹, Audie B. Oliquino ¹, Leila A. Gano ²

Affiliations
1 Polytechnic University of the Philippines - Sta. Mesa, Manila, PH
2 Philippine College of Health and Science - Claro M. Recto Avenue, Manila, PH

Abstract

Objectives: The main objective of this study is to develop a best model that will forecast the electricity rates of distribution utilities in Luzon. Methods/Statistical Analysis: The secondary data of price, demand and supply of electricity of distribution utilities per region in Luzon from January 2015 to March 2017 that was used in the study were gathered from MERALCO that were obtained from the official website of Wholesale Electricity Spot Market (WESM). The data were analysed by the use of statistical software such as EViews7 and MATLAB. Findings: The data were analyzed by the use of statistical software such as EViews7 and MATLAB. The models used for forecasting the participants’ electric demand is ARIMA (1, 1, 1) for CAR, Region 1, Region 2, Region 3, Region 4B and Region 5. The models for forecasting the supply of electricity are: ARIMA (2, 1, 2) for CAR, ARIMA (3, 1, 1) for NCR and Region 1, ARIMA (3, 1, 2) for Region 2, ARIMA (1, 1, 1) for Region 3 and ARIMA (2, 1, 1) for Region 4A and Region 5. The forecasted demand of electricity of distribution utilities in every region for years 2017-2019 will continue to increase. The forecasted electric supply of distribution utilities in every region for years 2017-2019 will go through an increase and decrease from time to time but still trending upwards. Additionally, it was also shown that there were no significant differences between the actual and forecasted values. Application/Improvements: Using the model electricity demand and supply rates of distribution utilities in Luzon were forecasted for the next two years.

Keywords

ARIMA Modeling, Demand and Supply, Electricity Rates, Forecasting, Luzon

Full Text

   

Jackie D. Urrutia ¹, Alyssa R. Alair ¹, Sabrina A. Iglesias ¹, Rolan J. Malvar ¹, Edcon B. Baccay ¹, Audie B. Oliquino ¹, Leila A. Gano ²

Affiliations
1 Polytechnic University of the Philippines- Sta. Mesa, Manila, PH
2 Philippine College of Health and Science - Claro M. Recto Avenue, Manila, PH

Abstract

Objectives: Petroleum oil prices are determined by many factors and have a big impact on the global environment and economy. This study aims to forecast the Petroleum oil prices for 2013 to 2016. Methods/Statistical Analysis: The Premium Gasoline Price is forecasted using the factors: Crude Oil Prices in Dubai, Foreign Exchange, and Consumer Price Index; the Unleaded Gasoline Price is forecasted using the factors: Crude Oil Prices in Dubai, and Consumer Price Index; the Regular Gasoline is forecasted using the factors: Crude Oil Prices in Dubai, Consumer Price Index, and Producer Price Index; the AV Turbo is forecasted using the factors: Crude Oil Prices in Dubai, Consumer Price Index, and Inflation Rate; the Kerosene is forecasted using the factors: Crude Oil Prices in Dubai, Consumer Price Index, and Inflation Rate; the Diesel Oil is forecasted using the factors: Crude Oil Prices in Dubai, and Inflation Rate; the Fuel Oil is forecasted using the factors: Crude Oil Prices in Dubai, Inflation Rate, and Purchasing Power of Peso. The researchers used the monthly data of the variables starting from January 2001 to December 2012, which were gathered from Bangko Sentral ng Pilipinas, Index Mundi and Department of Energy. In forecasting the dependent variable, the researchers used the Multiple Linear Regression. Findings: Findings reveals that the six independent variables such as Crude Oil Price, Inflation Rate, Purchasing Power of Peso, Consumer Price Index, Producer Price Index, and Foreign Exchange have a significant relationship with each petroleum product prices. Application/Improvements: This paper will help the government in monitoring the Petroleum Products Prices.

Keywords

Bangko Sentral ng Pilipinas, Dubai Crude Oil, Forecasting, Multiple Linear Regressions, Petroleum Oil Prices

Full Text

   

Jackie D. Urrutia ¹, Sheryl V. Villaverde ¹, Nathalie T. Algario ¹, Rolan J. Malvar ¹, Audie B. Oliquino ¹, Leila A. Gano ²

Affiliations
1 Polytechnic University of the Philippines, Santa Mesa, Manila, Kalakhang Maynila, PH
2 Philippine College of Health Science, Claro M. Recto Avenue, Metro Manila, PH

Abstract

Objectives: The main objective of this study is to forecast the number of fire for the years 2016 to 2020 that is possible to occur in the Philippines and to find the best fit multiple linear regression models for the said variable. Methods/Statistical analysis: The secondary data of Electrical Connections, Electrical Appliances, Spontaneous Combustion, Open Flame due to Unattended Cooking/Stove, Open Flame due to Torch, Open Flame due to Lighted Candle, Liquefied Petroleum Gas (LPG) Explosion due to Direct Flame Contact/Static Electricity, Lighted Cigarette Butt, Lighted Matchstick/Lighter, Under Investigation, and Others that was used in the study were gathered from Philippine bureau of fire protection. The data were analyzed by the use of statistical software such as EViews7 and MATLAB. Findings: In this paper, we showed that the independent variables namely, Electrical Connections, Electrical Appliances, Spontaneous Combustion, Open Flame due to Unattended Cooking/Stove, Open Flame due to Torch, Open Flame due to Lighted Candle, LPG Explosion due to Direct Flame Contact/Static Electricity, Lighted Cigarette Butt, Lighted Matchstick/Lighter, Under Investigation, and Others significantly affect the dependent variables which is the number of fire accidents. A normal estimation equation was derived to be the model that is best fit in predicting the number of fire for the year 2016 to 2020 through Multiple Linear Regression. Application/Improvements: This paper will be of help in raising the awareness of the citizen and the local government unit that is concerned in this matter to be prepared and allocate more effort in preventing a great damage caused by fire accidents.

Keywords

Fire Accidents, Forecasting, Multiple Linear Regression, Normal Estimation Equation, MATLAB

Full Text