Modeling steel supply and demand functions using logarithmic multiple regression analysis (case study: Steel industry in Iran)
The steel industry is considered as one of the mother industries that serves many large and small industries. Hence, recognizing the market situation of the steel industry both inside and outside the country is very important. Supply and demand are among the most important factors in stimulating the...
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| Veröffentlicht in: | Resources policy 2019-10, Vol.63, p.101409, Article 101409 |
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| creator | Mehmanpazir, Farhad Khalili-Damghani, Kaveh Hafezalkotob, Ashkan |
| description | The steel industry is considered as one of the mother industries that serves many large and small industries. Hence, recognizing the market situation of the steel industry both inside and outside the country is very important. Supply and demand are among the most important factors in stimulating the steel market. Usually, supply and demand have complex function. So, modeling and forecasting steel supply and demand require the use of accurate and scientific approaches. This paper presents an approach to identify the steel supply and demand functions and also to forecast the supply and demand trends. In the first step, through reviewing the historical data on the steel supply and demand in Iran, the effective and most important variables will be identified. Then, the supply and demand functions will be fitted using multiple logarithmic regression analysis. Logarithmically transforming variables in a regression model is a very common way to handle situations where a non-linear relationship exists between the independent and dependent variables. The accuracy of estimations is checked through appropriate statistical tests. The analysis is based on data of Iran steel market obtained from a 60 monthly period starting in 2010 and ending in 2014. The results showed that the estimated functions was appropriate in modeling the steel supply and demand behavior. The extrapolation analysis using 24 monthly data from 2017-2018 has also been accomplished to check the performance of the regression analysis.
•Supply and demand are among the most important factors in stimulating the steel market.•We presents an approach to identify the steel supply and demand functions.•Supply and demand functions will be fitted using multiple logarithmic regression analysis.•The accuracy of estimations are checked through appropriate statistical tests.•The research is based on Iran steel market for a 48-month period starting in 2010 and ending in 2014. |
| doi_str_mv | 10.1016/j.resourpol.2019.101409 |
| format | Article |
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•Supply and demand are among the most important factors in stimulating the steel market.•We presents an approach to identify the steel supply and demand functions.•Supply and demand functions will be fitted using multiple logarithmic regression analysis.•The accuracy of estimations are checked through appropriate statistical tests.•The research is based on Iran steel market for a 48-month period starting in 2010 and ending in 2014.</description><identifier>ISSN: 0301-4207</identifier><identifier>EISSN: 1873-7641</identifier><identifier>DOI: 10.1016/j.resourpol.2019.101409</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Analysis ; Case studies ; Data ; Demand analysis ; Demand curves ; Demand function ; Dependent variables ; Economic forecasting ; Extrapolation ; Forecasting ; Historical metallurgy ; Independent variables ; Iron and steel industry ; Logarithmic multiple regression ; Markets ; Metal industry ; Modelling ; Multiple regression analysis ; Regression analysis ; Regression models ; Statistical analysis ; Statistical tests ; Steel industry ; Supply & demand ; Supply function ; Variables</subject><ispartof>Resources policy, 2019-10, Vol.63, p.101409, Article 101409</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Oct 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c442t-8d5cae20a1306319b0307a20a5a4ba62a62e7c6f3dc3b8de747c42bf589466653</citedby><cites>FETCH-LOGICAL-c442t-8d5cae20a1306319b0307a20a5a4ba62a62e7c6f3dc3b8de747c42bf589466653</cites><orcidid>0000-0002-2338-1673</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0301420719300583$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27843,27901,27902,65534</link.rule.ids></links><search><creatorcontrib>Mehmanpazir, Farhad</creatorcontrib><creatorcontrib>Khalili-Damghani, Kaveh</creatorcontrib><creatorcontrib>Hafezalkotob, Ashkan</creatorcontrib><title>Modeling steel supply and demand functions using logarithmic multiple regression analysis (case study: Steel industry in Iran)</title><title>Resources policy</title><description>The steel industry is considered as one of the mother industries that serves many large and small industries. Hence, recognizing the market situation of the steel industry both inside and outside the country is very important. Supply and demand are among the most important factors in stimulating the steel market. Usually, supply and demand have complex function. So, modeling and forecasting steel supply and demand require the use of accurate and scientific approaches. This paper presents an approach to identify the steel supply and demand functions and also to forecast the supply and demand trends. In the first step, through reviewing the historical data on the steel supply and demand in Iran, the effective and most important variables will be identified. Then, the supply and demand functions will be fitted using multiple logarithmic regression analysis. Logarithmically transforming variables in a regression model is a very common way to handle situations where a non-linear relationship exists between the independent and dependent variables. The accuracy of estimations is checked through appropriate statistical tests. The analysis is based on data of Iran steel market obtained from a 60 monthly period starting in 2010 and ending in 2014. The results showed that the estimated functions was appropriate in modeling the steel supply and demand behavior. The extrapolation analysis using 24 monthly data from 2017-2018 has also been accomplished to check the performance of the regression analysis.
•Supply and demand are among the most important factors in stimulating the steel market.•We presents an approach to identify the steel supply and demand functions.•Supply and demand functions will be fitted using multiple logarithmic regression analysis.•The accuracy of estimations are checked through appropriate statistical tests.•The research is based on Iran steel market for a 48-month period starting in 2010 and ending in 2014.</description><subject>Analysis</subject><subject>Case studies</subject><subject>Data</subject><subject>Demand analysis</subject><subject>Demand curves</subject><subject>Demand function</subject><subject>Dependent variables</subject><subject>Economic forecasting</subject><subject>Extrapolation</subject><subject>Forecasting</subject><subject>Historical metallurgy</subject><subject>Independent variables</subject><subject>Iron and steel industry</subject><subject>Logarithmic multiple regression</subject><subject>Markets</subject><subject>Metal industry</subject><subject>Modelling</subject><subject>Multiple regression analysis</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>Steel industry</subject><subject>Supply & demand</subject><subject>Supply function</subject><subject>Variables</subject><issn>0301-4207</issn><issn>1873-7641</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>7TQ</sourceid><recordid>eNqFkE1LxDAQhoMouH78BgNe9NA1adOk603EL1A8qOeQTaZrlmxbM63Qi7_d1BWvwsBMwvO-zLyEnHA254zLi_U8ArZD7NowzxlfTL-CLXbIjFeqyJQUfJfMWMF4JnKm9skB4poxVqpKzsjXU-sg-GZFsQcIFIeuCyM1jaMONlOrh8b2vm2QDjhxoV2Z6Pv3jbd0M4TedwFohFXaAhOWpCaM6JGeWYOQbAc3XtKXH3ffuAH7OKaBPkTTnB-RvdoEhOPffkjebm9er--zx-e7h-urx8wKkfdZ5UprIGeGF0wWfLFM5yiT3qURSyPzVKCsrAtni2XlQAllRb6sy2ohpJRlcUhOt75dbD8GwF6vU2RpU9R5wQTniZGJUlvKxhYxQq276DcmjpozPYWt1_ovbD2FrbdhJ-XVVgnpiE8PUaP10FhwPoLttWv9vx7fxIGPAw</recordid><startdate>201910</startdate><enddate>201910</enddate><creator>Mehmanpazir, Farhad</creator><creator>Khalili-Damghani, Kaveh</creator><creator>Hafezalkotob, Ashkan</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TA</scope><scope>7TQ</scope><scope>8BJ</scope><scope>8FD</scope><scope>DHY</scope><scope>DON</scope><scope>FQK</scope><scope>JBE</scope><scope>JG9</scope><orcidid>https://orcid.org/0000-0002-2338-1673</orcidid></search><sort><creationdate>201910</creationdate><title>Modeling steel supply and demand functions using logarithmic multiple regression analysis (case study: Steel industry in Iran)</title><author>Mehmanpazir, Farhad ; Khalili-Damghani, Kaveh ; Hafezalkotob, Ashkan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c442t-8d5cae20a1306319b0307a20a5a4ba62a62e7c6f3dc3b8de747c42bf589466653</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Analysis</topic><topic>Case studies</topic><topic>Data</topic><topic>Demand analysis</topic><topic>Demand curves</topic><topic>Demand function</topic><topic>Dependent variables</topic><topic>Economic forecasting</topic><topic>Extrapolation</topic><topic>Forecasting</topic><topic>Historical metallurgy</topic><topic>Independent variables</topic><topic>Iron and steel industry</topic><topic>Logarithmic multiple regression</topic><topic>Markets</topic><topic>Metal industry</topic><topic>Modelling</topic><topic>Multiple regression analysis</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Statistical analysis</topic><topic>Statistical tests</topic><topic>Steel industry</topic><topic>Supply & demand</topic><topic>Supply function</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mehmanpazir, Farhad</creatorcontrib><creatorcontrib>Khalili-Damghani, Kaveh</creatorcontrib><creatorcontrib>Hafezalkotob, Ashkan</creatorcontrib><collection>CrossRef</collection><collection>Materials Business File</collection><collection>PAIS Index</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>PAIS International</collection><collection>PAIS International (Ovid)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>Materials Research Database</collection><jtitle>Resources policy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mehmanpazir, Farhad</au><au>Khalili-Damghani, Kaveh</au><au>Hafezalkotob, Ashkan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling steel supply and demand functions using logarithmic multiple regression analysis (case study: Steel industry in Iran)</atitle><jtitle>Resources policy</jtitle><date>2019-10</date><risdate>2019</risdate><volume>63</volume><spage>101409</spage><pages>101409-</pages><artnum>101409</artnum><issn>0301-4207</issn><eissn>1873-7641</eissn><abstract>The steel industry is considered as one of the mother industries that serves many large and small industries. Hence, recognizing the market situation of the steel industry both inside and outside the country is very important. Supply and demand are among the most important factors in stimulating the steel market. Usually, supply and demand have complex function. So, modeling and forecasting steel supply and demand require the use of accurate and scientific approaches. This paper presents an approach to identify the steel supply and demand functions and also to forecast the supply and demand trends. In the first step, through reviewing the historical data on the steel supply and demand in Iran, the effective and most important variables will be identified. Then, the supply and demand functions will be fitted using multiple logarithmic regression analysis. Logarithmically transforming variables in a regression model is a very common way to handle situations where a non-linear relationship exists between the independent and dependent variables. The accuracy of estimations is checked through appropriate statistical tests. The analysis is based on data of Iran steel market obtained from a 60 monthly period starting in 2010 and ending in 2014. The results showed that the estimated functions was appropriate in modeling the steel supply and demand behavior. The extrapolation analysis using 24 monthly data from 2017-2018 has also been accomplished to check the performance of the regression analysis.
•Supply and demand are among the most important factors in stimulating the steel market.•We presents an approach to identify the steel supply and demand functions.•Supply and demand functions will be fitted using multiple logarithmic regression analysis.•The accuracy of estimations are checked through appropriate statistical tests.•The research is based on Iran steel market for a 48-month period starting in 2010 and ending in 2014.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.resourpol.2019.101409</doi><orcidid>https://orcid.org/0000-0002-2338-1673</orcidid></addata></record> |
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| subjects | Analysis Case studies Data Demand analysis Demand curves Demand function Dependent variables Economic forecasting Extrapolation Forecasting Historical metallurgy Independent variables Iron and steel industry Logarithmic multiple regression Markets Metal industry Modelling Multiple regression analysis Regression analysis Regression models Statistical analysis Statistical tests Steel industry Supply & demand Supply function Variables |
| title | Modeling steel supply and demand functions using logarithmic multiple regression analysis (case study: Steel industry in Iran) |
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