Natural Gas Consumption and Temperature: Balanced and Unbalanced Panel Data Regression Model

Natural Gas Consumption and Temperature: Balanced and Unbalanced Panel Data Regression Model TOORAJ KARIMI, A.RASOL NIKNAM, M. REZA SADEGHI MOGHADAM Management Faculty Tehran universy Tehran, Iran IRAN tkarimi@ut.ac.ir Abstract: Although the share of petroleum products in Iran reduced to 47% in 2007 from 64% in 1991, the share of natural gas consumption increased to 46% from 26% in this period. On the other hand the combination of natural gas consumption in 2007 shows that the power plants are the biggest users by obtaining 35.6% of Iran gas consumption, domestic and commercial sectors are the next users wh a share of 32.3% and 27.1% and the public sector allocates 5% of total shares, so is important to pay attention to the effective factors on natural gas consumption in the country and also measure the rate of their affectivy on gas consumption in different sectors. Since Temperature changes influence the changes in natural gas consumption of various sectors differently, panel data regression model is used to identify relations between temperature and consumption of each sector in this research. Finally for the purpose of enhancing accuracy of the model, after eliminating parts of data, the process of selecting and estimating of models are repeated for sectors and the results are compared wh each other. KeyWords: Balanced Panel Data, Unbalanced Panel Data, Gas Consumption, Temperature effect, Iran 1 Introduction In order to meet the policy of gas replacement wh other energy carriers in Iran, many extensive efforts have been tried for further development of gas transmission system throughout the country. National Iranian Gas Company (NIGC) undertakes to meet this goal by creating a gas transmission system in many different regions of the country so the natural gas consumption reached the average annual growth of 12% during 1990s in Iran while the global gas consumption in this period had the annual growth of 1.8%. The fully different climatic regions in Iran have caused the various provinces having variable needs for gas consumption; furthermore, gas consumption has the different parts including domestic, commercial, industrial, agricultural, transportation, power plant and main industries consumption sectors. The combination of natural gas consumption in 2007 shows that the power plants are the biggest users by obtaining 37.6% of total collection of gas consumption. Enjoying a share of 33.3% and 29.1% of total collection of gas consumption, domestic, commercial and industrial sectors are the next users but the public sector allocates 5% of total shares [1]. The subject of energy consumption especially natural gas and s relation to the different economic measures is one of the subjects has been discussed by the authories in the field of energy management. Some of these researches concluded in the increasing energy consumption particularly natural gas as the reason of increasing income of a country and some addressed to the adverse results in other cases [2]. There are many researches for determining cause and effect relations between different factors and energy consumption especially gas, such as You & Chuo in South Korea, Philippines and also the studies of Huang & Gum in Taiwan [3,4]. In the studies carried out about forecasting gas consumption by now, different variables have been considered as the effective elements on gas supply and demand. For example, the rate of gas supply and demand were defined as the effective factors on gas consumption in Ankara, therefore the gas price and number of consumers were used in the mentioned model. Moreover, due to the importation of Ankara gas, the foreign exchange rate was considered as a factor showing the abily of families to buy, consumption methods and culture of society were used as other effective factors [5]. Kraft emphasized the socialeconomic indices in his study on the energy consumption in USA [6]. Natural gas consumption in the Uned States was studied by Sailor and since the rate of gas consumption depends on the number of users so ISBN: 9781618041227 81

instead of temperature the populationweighted average of temperature was considered monthly after dividing each state into different sectors based on climatic condion [7]. Totally is believed that although temperature and climatic changes play a significant role in many researches but in the countries where the price of gas consumption bears higher changes and supplying their gas consumption is mainly through importation from other countries, price and foreign exchange rate are efficient factors. In this research according to nonchangeabily of price of natural gas in Iran and also the production of main amount of gas in the country, temperature changes effect on natural gas consumption in various sectors and also coincidently in different provinces is investigated. Almost all researches carried out for evaluating factors effective on gas demand or forecasting demand have eher considered a part of consumption in all state provinces, for example domestic consumption, or studied the different parts in a province, because the statistical methods used have no abily to analyze the different parts of gas consumption in all state provinces simultaneously. Regarding this, panel data regression models are used in this research. Developments of Panel Data Econometric methods during last decade have made possible to estimate energy management models by combining timeseries and crosssections data. Panel data is a set of data including several phenomena during several time periods. In other words, n crosssectional un is observed in T time period under a collection of integrated data. So, we will have nt data. The basic Can the observations be described as being a random sample from a given population? framework of panel data regression model is as follows [6]: y = X β + z α+ ε i (1) Where there are K regressors. i and t are sample crosssection and time series uns, β is estimated parameter and ε are error terms and the heterogeney. z i α is individual effect where z i contains a constant term and a set of individual or group specific variables, which may be observed, such as race, sex, location, and so on or unobserved, such as family specific characteristics, individual heterogeney in skill or preferences, and so on, all of which are taken to be constant over time t [8]. Three common regression techniques are used to estimate model by panel data including: Pooled Ordinary Least Squares Model, The Fixed Effects Model (Least Squares Dummy Variable Model) and The s Model (Error Components Model). This research is about measuring natural gas consumption as a factor of energy demand (econometrics) responding to temperature increases and the main aim is making a unique model for each consumption sector wh consideration of provinces of Iran, so the rest of paper is organized as follows: section 2 defines the methodology of model selection and Section 3 explains data retrieval. Section 4 and 5 detail Balanced and unbalanced Panel Data models and the final conclusions are given in Section 6. YES NO Perform both fixed effects and random effects regressions. Use fixed effects Does a Hausman test indicate significant differences in the coefficients? NO Provisionally choose random effects. Does a Fisher test indicate the presence of random effects? YES YES NO Use fixed effects Use random effects Use pooled OLS Fig. 1 Process of Decision Making about Modell Selection ISBN: 9781618041227 82

2 Methodology for selecting a suable model It is necessary to select a proper panel data model among three mentioned models in various researches. The process of panel data model selection for this research is illustrated in Figure (1) [9]. At first step Hausman test should be used to distinguish between the fixed and random effects models. This test investigates assumption of lack of correlation between regressors and specific individual random effects [10]. If Hausman test result in random effect model, we should investigate whether there are unobserved effects. In the other word, this question that whether the Pooled Ordinary Least Squares is suable or s Model shall be replied. Therefore, we can test the hypothesis that the constant terms are all equal wh an F test. Under the null hypothesis of equaly, the efficient estimator is pooled least squares [8]. 3 Data retrieval To study the effect of temperature on gas consumption, the data of 24 months (from April 2009 to March 2011) was studied for 25 provinces and the information of 6 sectors: domestic, industrial, agricultural, power plant, public and trading, was collected. As populationweighted average of temperature is considered instead of temperature so the average day temperature for each center of provinces is multiplied in the number of users in that province in the same day (wt), and divides total wt obtained for 25 provinces in total number of users in the country in the same day. The obtained result is pwt, or population average of temperature in the same day. To obtain a monthly pwt for country, we conclude the average of 30 days. Table.1. results of Balanced Panel Data regression Consumption Sector Domestic Industrial Agricultural Power Plant Public commercial Transportation Total Hausman Test pvalue 0.0748 0.0301 0.4377 0.2014 0.1150 0.1612 0.1869 0.4847 F test pvalue Suable panel data regression Fixed Effect Coefficient 8313170. 62975.39 29157.99 5503517. 345088.5 461113.0 44927.29 160.4463 Adjusted RSquared 0.704456 0.913353 0.744455 0.814149 0.745374 0.831427 0.895948 0.874876 Probabily Error 00 0.7634 00 00 00 00 00 64 Table.2. results of Unbalanced Panel Data regression Consumption Sector Domestic Industrial Agricultural Power Plant Public Commercial Transportation Total Hausman Test pvalue 0.516685 0.744306 0.453377 0.650312 0.753115 0.783123 F test pvalue Suable panel data regression Coefficient 8308732. 58534.73 30914.88 350534.2 464673.8 49156.51 Adjusted RSquared 0.742650 0.900636 0.744523 0.771881 0.839182 0.892832 Probabily Error 00 0.8024 00 00 00 00 4 Balanced panel data modelling The collection of data related to observations of a phenomenon during different periods are called TimeSeries and those consisting of the observations of several phenomena in a time are called Cross Sectional Data. If there are observations of each phenomenon during define time periods, the data are called Balanced Panel Data and because the groups are equal in size in analysis of balanced panel data, total size of samples is nt [8]. In this research, the provinces and months constute the CrossSection (i) and TimeSeries (t) data of panel data regression model and process of Figure (1) was executed separately for 600 samples (25 provinces and 24 month) of each sector. Also, total annual consumption of each province from 2002 to 2010, totally 225 samples (25 provinces and 9 years) was studied and process of Figure (1) was executed. ISBN: 9781618041227 83

Eviews Software was used to perform all processes of regression tests and results from these tests have been shown in Table 1. The results from Hausman tests reject the zero hypotheses in 95 per cent significant for regressions of all sectors except the industrial sector, representing lack of correlation between regressors and specific individual random effects. Therefore, is necessary to use random effects regression for them. But, fixed effects regression shall be applied for industrial sector. The hypothesis of equaly of fixed effects for all provinces using F statistic was tested to select among pooled ordinary least squares and random effect methods. It was performed for all sectors except the industrial sector. pvalue from F tests of all regressions were obtained equal to zero, which reject the null hypothesis showing equaly of fixed effects for all provinces and is essential to use the random effects model. The complete result of regression model of industry sector is represented in Appendices (1). Coefficients of regression model in each sector indicate type and intensy of relationship between temperature and gas consumption in that sector. The minus and plus signs of coefficients show reversible and direct relation. The adjusted Rsquared shows explanatory strength of regressors. Since significant level is 95% in this study, therefore more than 5% probabily error shows that relationship between temperature and consumption is not significant. We can see that significant level of all sectors except industry is lower than 5% and shows, there is no relationship between temperature and gas consumption of this sector. 5 Unbalanced panel data modelling If the observations of each phenomenon appear in different periods, for example, the first phenomenon in 2 periods and the second in 4, they are called Unbalanced Panel Data. A total sample in unbalanced panel data is n i = 1 Ti [8]. According to the studies of Shakouri & et al, is obvious that energy consumption increase as temperature increases or decreases during the hot or cool days in the year. Naturally, the rate of this increase depends on the deference between momentum temperature and normal temperature [11]. Figure (2) is drawn according to the total gas consumptions except power plants that the least gas consumption appears in the temperature of 2930 o. So to promote precision in calculations in all available consumption data for all provinces except power plant, all data of temperature upper than 30 o have been omted. After omting data related to 30 o temperature, 552 remaining data in all parts, except the power plant, and total consumption have been analysed again according to the process of Fig. 1. At first step, Hausman test is used to choose from random effects regression and the null hypothesis in 95 per cent significant is rejected for all sectors to represent lack of correlation between regressors and specific individual random effects. Therefore, is necessary for estimating the regressions to use random effects regression. Next step, the hypothesis of equaly of fixed effects for all provinces using F statistic is tested to select among Pooled Ordinary Least Squares and method. As shown in table (2), pvalue from F tests of all regressions were obtained equal to zero and is essential to use the random effects model for all sectors. The complete result of regression model of industry sector is represented in Appendices (2). By comparing tables 1 & 2 can be understood that determinative coefficient in domestic and public sectors increased about 4% and there was no change in agricultural, commercial and transportation sectors. Also, there is no significant relationship between temperature and consumption in industrial sector. Fig. 2. Temperature and gas consumption ISBN: 9781618041227 84

6 Conclusion According to the results shown in tables of balanced and unbalanced panel data, there is an adverse relationship between gas consumption and temperature in transportation, commercial, public, agricultural and domestic sectors. The highest relationship is in transportation sector wh adjusted coefficient of 89% and the least coefficient is 70% in domestic sector which shows that 70 per cent of changes in consumption in this sector are due to the temperature changes. In power plant sector, there is a direct relation between gas consumption and temperature so that 81% of consumption changes are related to temperature changes. Since the gas is used as a production factor in industrial sector of the country so s consumption does not depend on the temperature. It is obvious that the results of balanced and unbalanced panel data models are relatively similar and there is no significant difference between them. Although there were limed changes in coefficient but Adjusted R Squared coefficients had negligible changes and direction and relationship between consumption of different sectors and temperature had no changes. models for the Uned States, energy 23 (2), 9110. [8] Greene, H.W., (2010), Econometric Analysis, Prentice Hall,New Jersey, Chapter 13, pp 283338X. [9] Dougherty, C., (2007), introduction to econometrics, oxford universy press, 4th Ed, chapter 14, pp 408421. [10] Hsiao, C., (2011), analysis of panel data, Cambridge universy press, 3th ed, chapter 1, pp2. [11] Shakouri, H., Nadimi, G. R., Ghaderi, F., (2009), A hybrid TSKFR model to study shortterm variations of the electricy demand versus the temperature changes, Expert Systems wh Applications 36, 1765 1772. References: [1] http://www.nigc.ir/se.aspx?partree=111s 11 [2] Belloumi,M., (2011), Energy consumption and GDP in Tunisia: Cointegration and causaly analysis, Energy Policy 37, 2745 2753. [3] Yu, S.H., Choi, J.Y. (1985), The causal relationship between energy and GDP: an international comparison, Journal of Energy and Development 10 (2), 249 272. [4] Hwang, D., Gum, B. (1992), The causal relationship between energy and GDP: the case of Taiwan, Journal of Energy and Development Spring, 219 226. [5] Gorucu. F.B, Gumrah. F, (2004), Evaluation and Forecasting of Gas Consumption by Statistical, Energy Sources, 26, 267 276. [6] Kraft. J, Kraft. A, (1978), On the relationship between energy and GDP, Journal of Energy and Development, 3, 401 403. [7] Sailor, D.J., Rosen, J.N., Ricardo, j., (1997), natural gas consumption and climate: a comprehensive set of predictive statelevel ISBN: 9781618041227 85

Appendix (1) Dependent Variable: INDU? Method: Pooled Least Squares Date: 08/03/12 Time: 20:28 Sample: 1986:01 1987:12 Included observations: 24 Total panel (balanced) observations 600 Variable Coefficient Std. Error tstatistic Prob. TEMP? 62975.39 209109.7 0.301160 0.7634 Fixed Effects _1C 90959691 _2C 13517471 _3C 13094312 _4C 2.39E+08 _5C 5.73E+08 _6C 22786998 _7C 1.25E+08 _8C 24147972 _9C 4.89E+08 _10C 15371821 _11C 28972571 _12C 4.03E+08 _13C 47624507 _14C 22029736 _15C 9899827. _16C 1.34E+08 _17C 46077193 _18C 10205664 _19C 8089369. _20C 22105975 _21C 14625830 _22C 26199996 _23C 1.49E+08 _24C 22924919 _25C 29850604 Rsquared 0.916969 Mean dependent var 1.02E+08 Adjusted Rsquared 0.913353 S.D. dependent var 1.61E+08 S.E. of regression 47411591 Sum squared resid 1.29E+18 DurbinWatson stat 0.402706 Appendix (2) Dependent Variable: INDU? Method: GLS (Variance Components) Date: 10/17/12 Time: 17:16 Sample: 1986:01 1987:12 Included observations: 24 Total panel (unbalanced) observations 552 Variable Coefficient Std. Error tstatistic Prob. C 94558141 31763375 2.976955 30 TEMP? 58534.73 233775.6 0.250389 0.8024 s _1C 2917282. _2C 80549852 _3C 81163879 _4C 1.47E+08 _5C 4.74E+08 _6C 71517638 _7C 30475542 _8C 70172394 _9C 4.00E+08 _10C 78902763 _11C 65819291 _12C 2.99E+08 _13C 46799781 _14C 71411375 _15C 84365703 _16C 39007011 _17C 48344815 _18C 84064185 _19C 86182525 _20C 72218226 _21C 79759655 _22C 68149294 _23C 54275722 _24C 71384220 _25C 63844812 GLS Transformed Regression Rsquared 0.900816 Mean dependent var 93670123 Adjusted Rsquared 0.900636 S.D. dependent var 1.53E+08 S.E. of regression 48175454 Sum squared resid 1.28E+18 DurbinWatson stat 0.383962 Unweighted Statistics including s Rsquared 0.904620 Mean dependent var 93670123 Adjusted Rsquared 0.904447 S.D. dependent var 1.53E+08 S.E. of regression 47242691 Sum squared resid 1.23E+18 DurbinWatson stat 0.399273 ISBN: 9781618041227 86