Time series modelling for steel production

Steel and its production has now become an important ingredient for the economy of emerging countries. In this process, it is also very important to develop sophisticated forecast techniques for steel production in order to determine the real growth rate of an economy. Especially in South Asia where steel and its production, is one of the largest segment of the economy. Majority population of this region has associated directly or indirectly with steel industry. Inspite the importance of steel for this region, no conclusive research work has done related to this issue. This study investigates the forecasts of steel production in Pakistan for the first time, which is an important emerging economy of the South Asian region. Prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The auto regressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model.This paper forecasts steel production by using different time series ARIMA models. A data set of steel production of Pakistan from 1972 -2010 is used for the analysis. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (1,1,4) is suitable model for prediction of steel production in this case. It is concluded that model- 3 of this study is the best model to forecast the production of steel in Pakistan. After checking all the test it is come to know that the data is stationary at first difference and AR(1), MA(1), MA(2), MA(3) and MA(4) with first order is suitable for forecasting the production of steel. The forecasted values obtained from model- 3 are closer to the actual values as compared to the other model. The forecasted value of 2014 show that the production of steel in that particular year appeared to be least as compared to the production during the last five years. These results suggest that the policy makers and planning division of the country must give attention toward this matter and try to find out the reasons of low productivity of steel in the country. It is also an opportunity for the policy makers to develop policies which may help the steel production in future. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ikram, Amir [verfasserIn] Su, Qin Rafiq, Muhammad Yasir Ramiz-Ur-Rehman

Format:	Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Metals Steel production Carbon Time series Researchers Heat resistance Studies Production increases Energy consumption Steel industry Iron Forecasting techniques

Übergeordnetes Werk:	Enthalten in: The journal of developing areas - Nashville, Tenn. : [Verlag nicht ermittelbar], 1966, 50(2016), 3, Seite 191-207
Übergeordnetes Werk:	volume:50 ; year:2016 ; number:3 ; pages:191-207

Links:	Volltext Link aufrufen

DOI / URN:	10.1353/jda.2016.0112

Katalog-ID:	OLC1978801564

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520			\|a Steel and its production has now become an important ingredient for the economy of emerging countries. In this process, it is also very important to develop sophisticated forecast techniques for steel production in order to determine the real growth rate of an economy. Especially in South Asia where steel and its production, is one of the largest segment of the economy. Majority population of this region has associated directly or indirectly with steel industry. Inspite the importance of steel for this region, no conclusive research work has done related to this issue. This study investigates the forecasts of steel production in Pakistan for the first time, which is an important emerging economy of the South Asian region. Prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The auto regressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model.This paper forecasts steel production by using different time series ARIMA models. A data set of steel production of Pakistan from 1972 -2010 is used for the analysis. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (1,1,4) is suitable model for prediction of steel production in this case. It is concluded that model- 3 of this study is the best model to forecast the production of steel in Pakistan. After checking all the test it is come to know that the data is stationary at first difference and AR(1), MA(1), MA(2), MA(3) and MA(4) with first order is suitable for forecasting the production of steel. The forecasted values obtained from model- 3 are closer to the actual values as compared to the other model. The forecasted value of 2014 show that the production of steel in that particular year appeared to be least as compared to the production during the last five years. These results suggest that the policy makers and planning division of the country must give attention toward this matter and try to find out the reasons of low productivity of steel in the country. It is also an opportunity for the policy makers to develop policies which may help the steel production in future.
650		4	\|a Metals
650		4	\|a Steel production
650		4	\|a Carbon
650		4	\|a Time series
650		4	\|a Researchers
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650		4	\|a Steel industry
650		4	\|a Iron
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700	1		\|a Rafiq, Muhammad Yasir \|4 oth
700	0		\|a Ramiz-Ur-Rehman \|4 oth
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allfields	10.1353/jda.2016.0112 doi PQ20161012 (DE-627)OLC1978801564 (DE-599)GBVOLC1978801564 (PRQ)c664-f1d799bc7d4445bd69bd047953587c4b82641e7e8a545867ac400d4539ee70f40 (KEY)0018259120160000050000300191timeseriesmodellingforsteelproduction DE-627 ger DE-627 rakwb eng 320 DNB Ikram, Amir verfasserin aut Time series modelling for steel production 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Steel and its production has now become an important ingredient for the economy of emerging countries. In this process, it is also very important to develop sophisticated forecast techniques for steel production in order to determine the real growth rate of an economy. Especially in South Asia where steel and its production, is one of the largest segment of the economy. Majority population of this region has associated directly or indirectly with steel industry. Inspite the importance of steel for this region, no conclusive research work has done related to this issue. This study investigates the forecasts of steel production in Pakistan for the first time, which is an important emerging economy of the South Asian region. Prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The auto regressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model.This paper forecasts steel production by using different time series ARIMA models. A data set of steel production of Pakistan from 1972 -2010 is used for the analysis. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (1,1,4) is suitable model for prediction of steel production in this case. It is concluded that model- 3 of this study is the best model to forecast the production of steel in Pakistan. After checking all the test it is come to know that the data is stationary at first difference and AR(1), MA(1), MA(2), MA(3) and MA(4) with first order is suitable for forecasting the production of steel. The forecasted values obtained from model- 3 are closer to the actual values as compared to the other model. The forecasted value of 2014 show that the production of steel in that particular year appeared to be least as compared to the production during the last five years. These results suggest that the policy makers and planning division of the country must give attention toward this matter and try to find out the reasons of low productivity of steel in the country. It is also an opportunity for the policy makers to develop policies which may help the steel production in future. Metals Steel production Carbon Time series Researchers Heat resistance Studies Production increases Energy consumption Steel industry Iron Forecasting techniques Su, Qin oth Rafiq, Muhammad Yasir oth Ramiz-Ur-Rehman oth Enthalten in The journal of developing areas Nashville, Tenn. : [Verlag nicht ermittelbar], 1966 50(2016), 3, Seite 191-207 (DE-627)129551198 (DE-600)219414-4 (DE-576)015004902 0022-037X nnns volume:50 year:2016 number:3 pages:191-207 http://dx.doi.org/10.1353/jda.2016.0112 Volltext http://search.proquest.com/docview/1801554294 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-IBL GBV_ILN_26 AR 50 2016 3 191-207
spelling	10.1353/jda.2016.0112 doi PQ20161012 (DE-627)OLC1978801564 (DE-599)GBVOLC1978801564 (PRQ)c664-f1d799bc7d4445bd69bd047953587c4b82641e7e8a545867ac400d4539ee70f40 (KEY)0018259120160000050000300191timeseriesmodellingforsteelproduction DE-627 ger DE-627 rakwb eng 320 DNB Ikram, Amir verfasserin aut Time series modelling for steel production 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Steel and its production has now become an important ingredient for the economy of emerging countries. In this process, it is also very important to develop sophisticated forecast techniques for steel production in order to determine the real growth rate of an economy. Especially in South Asia where steel and its production, is one of the largest segment of the economy. Majority population of this region has associated directly or indirectly with steel industry. Inspite the importance of steel for this region, no conclusive research work has done related to this issue. This study investigates the forecasts of steel production in Pakistan for the first time, which is an important emerging economy of the South Asian region. Prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The auto regressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model.This paper forecasts steel production by using different time series ARIMA models. A data set of steel production of Pakistan from 1972 -2010 is used for the analysis. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (1,1,4) is suitable model for prediction of steel production in this case. It is concluded that model- 3 of this study is the best model to forecast the production of steel in Pakistan. After checking all the test it is come to know that the data is stationary at first difference and AR(1), MA(1), MA(2), MA(3) and MA(4) with first order is suitable for forecasting the production of steel. The forecasted values obtained from model- 3 are closer to the actual values as compared to the other model. The forecasted value of 2014 show that the production of steel in that particular year appeared to be least as compared to the production during the last five years. These results suggest that the policy makers and planning division of the country must give attention toward this matter and try to find out the reasons of low productivity of steel in the country. It is also an opportunity for the policy makers to develop policies which may help the steel production in future. Metals Steel production Carbon Time series Researchers Heat resistance Studies Production increases Energy consumption Steel industry Iron Forecasting techniques Su, Qin oth Rafiq, Muhammad Yasir oth Ramiz-Ur-Rehman oth Enthalten in The journal of developing areas Nashville, Tenn. : [Verlag nicht ermittelbar], 1966 50(2016), 3, Seite 191-207 (DE-627)129551198 (DE-600)219414-4 (DE-576)015004902 0022-037X nnns volume:50 year:2016 number:3 pages:191-207 http://dx.doi.org/10.1353/jda.2016.0112 Volltext http://search.proquest.com/docview/1801554294 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-IBL GBV_ILN_26 AR 50 2016 3 191-207
allfields_unstemmed	10.1353/jda.2016.0112 doi PQ20161012 (DE-627)OLC1978801564 (DE-599)GBVOLC1978801564 (PRQ)c664-f1d799bc7d4445bd69bd047953587c4b82641e7e8a545867ac400d4539ee70f40 (KEY)0018259120160000050000300191timeseriesmodellingforsteelproduction DE-627 ger DE-627 rakwb eng 320 DNB Ikram, Amir verfasserin aut Time series modelling for steel production 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Steel and its production has now become an important ingredient for the economy of emerging countries. In this process, it is also very important to develop sophisticated forecast techniques for steel production in order to determine the real growth rate of an economy. Especially in South Asia where steel and its production, is one of the largest segment of the economy. Majority population of this region has associated directly or indirectly with steel industry. Inspite the importance of steel for this region, no conclusive research work has done related to this issue. This study investigates the forecasts of steel production in Pakistan for the first time, which is an important emerging economy of the South Asian region. Prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The auto regressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model.This paper forecasts steel production by using different time series ARIMA models. A data set of steel production of Pakistan from 1972 -2010 is used for the analysis. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (1,1,4) is suitable model for prediction of steel production in this case. It is concluded that model- 3 of this study is the best model to forecast the production of steel in Pakistan. After checking all the test it is come to know that the data is stationary at first difference and AR(1), MA(1), MA(2), MA(3) and MA(4) with first order is suitable for forecasting the production of steel. The forecasted values obtained from model- 3 are closer to the actual values as compared to the other model. The forecasted value of 2014 show that the production of steel in that particular year appeared to be least as compared to the production during the last five years. These results suggest that the policy makers and planning division of the country must give attention toward this matter and try to find out the reasons of low productivity of steel in the country. It is also an opportunity for the policy makers to develop policies which may help the steel production in future. Metals Steel production Carbon Time series Researchers Heat resistance Studies Production increases Energy consumption Steel industry Iron Forecasting techniques Su, Qin oth Rafiq, Muhammad Yasir oth Ramiz-Ur-Rehman oth Enthalten in The journal of developing areas Nashville, Tenn. : [Verlag nicht ermittelbar], 1966 50(2016), 3, Seite 191-207 (DE-627)129551198 (DE-600)219414-4 (DE-576)015004902 0022-037X nnns volume:50 year:2016 number:3 pages:191-207 http://dx.doi.org/10.1353/jda.2016.0112 Volltext http://search.proquest.com/docview/1801554294 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-IBL GBV_ILN_26 AR 50 2016 3 191-207
allfieldsGer	10.1353/jda.2016.0112 doi PQ20161012 (DE-627)OLC1978801564 (DE-599)GBVOLC1978801564 (PRQ)c664-f1d799bc7d4445bd69bd047953587c4b82641e7e8a545867ac400d4539ee70f40 (KEY)0018259120160000050000300191timeseriesmodellingforsteelproduction DE-627 ger DE-627 rakwb eng 320 DNB Ikram, Amir verfasserin aut Time series modelling for steel production 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Steel and its production has now become an important ingredient for the economy of emerging countries. In this process, it is also very important to develop sophisticated forecast techniques for steel production in order to determine the real growth rate of an economy. Especially in South Asia where steel and its production, is one of the largest segment of the economy. Majority population of this region has associated directly or indirectly with steel industry. Inspite the importance of steel for this region, no conclusive research work has done related to this issue. This study investigates the forecasts of steel production in Pakistan for the first time, which is an important emerging economy of the South Asian region. Prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The auto regressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model.This paper forecasts steel production by using different time series ARIMA models. A data set of steel production of Pakistan from 1972 -2010 is used for the analysis. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (1,1,4) is suitable model for prediction of steel production in this case. It is concluded that model- 3 of this study is the best model to forecast the production of steel in Pakistan. After checking all the test it is come to know that the data is stationary at first difference and AR(1), MA(1), MA(2), MA(3) and MA(4) with first order is suitable for forecasting the production of steel. The forecasted values obtained from model- 3 are closer to the actual values as compared to the other model. The forecasted value of 2014 show that the production of steel in that particular year appeared to be least as compared to the production during the last five years. These results suggest that the policy makers and planning division of the country must give attention toward this matter and try to find out the reasons of low productivity of steel in the country. It is also an opportunity for the policy makers to develop policies which may help the steel production in future. Metals Steel production Carbon Time series Researchers Heat resistance Studies Production increases Energy consumption Steel industry Iron Forecasting techniques Su, Qin oth Rafiq, Muhammad Yasir oth Ramiz-Ur-Rehman oth Enthalten in The journal of developing areas Nashville, Tenn. : [Verlag nicht ermittelbar], 1966 50(2016), 3, Seite 191-207 (DE-627)129551198 (DE-600)219414-4 (DE-576)015004902 0022-037X nnns volume:50 year:2016 number:3 pages:191-207 http://dx.doi.org/10.1353/jda.2016.0112 Volltext http://search.proquest.com/docview/1801554294 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-IBL GBV_ILN_26 AR 50 2016 3 191-207
allfieldsSound	10.1353/jda.2016.0112 doi PQ20161012 (DE-627)OLC1978801564 (DE-599)GBVOLC1978801564 (PRQ)c664-f1d799bc7d4445bd69bd047953587c4b82641e7e8a545867ac400d4539ee70f40 (KEY)0018259120160000050000300191timeseriesmodellingforsteelproduction DE-627 ger DE-627 rakwb eng 320 DNB Ikram, Amir verfasserin aut Time series modelling for steel production 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Steel and its production has now become an important ingredient for the economy of emerging countries. In this process, it is also very important to develop sophisticated forecast techniques for steel production in order to determine the real growth rate of an economy. Especially in South Asia where steel and its production, is one of the largest segment of the economy. Majority population of this region has associated directly or indirectly with steel industry. Inspite the importance of steel for this region, no conclusive research work has done related to this issue. This study investigates the forecasts of steel production in Pakistan for the first time, which is an important emerging economy of the South Asian region. Prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The auto regressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model.This paper forecasts steel production by using different time series ARIMA models. A data set of steel production of Pakistan from 1972 -2010 is used for the analysis. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (1,1,4) is suitable model for prediction of steel production in this case. It is concluded that model- 3 of this study is the best model to forecast the production of steel in Pakistan. After checking all the test it is come to know that the data is stationary at first difference and AR(1), MA(1), MA(2), MA(3) and MA(4) with first order is suitable for forecasting the production of steel. The forecasted values obtained from model- 3 are closer to the actual values as compared to the other model. The forecasted value of 2014 show that the production of steel in that particular year appeared to be least as compared to the production during the last five years. These results suggest that the policy makers and planning division of the country must give attention toward this matter and try to find out the reasons of low productivity of steel in the country. It is also an opportunity for the policy makers to develop policies which may help the steel production in future. Metals Steel production Carbon Time series Researchers Heat resistance Studies Production increases Energy consumption Steel industry Iron Forecasting techniques Su, Qin oth Rafiq, Muhammad Yasir oth Ramiz-Ur-Rehman oth Enthalten in The journal of developing areas Nashville, Tenn. : [Verlag nicht ermittelbar], 1966 50(2016), 3, Seite 191-207 (DE-627)129551198 (DE-600)219414-4 (DE-576)015004902 0022-037X nnns volume:50 year:2016 number:3 pages:191-207 http://dx.doi.org/10.1353/jda.2016.0112 Volltext http://search.proquest.com/docview/1801554294 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-IBL GBV_ILN_26 AR 50 2016 3 191-207
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source	Enthalten in The journal of developing areas 50(2016), 3, Seite 191-207 volume:50 year:2016 number:3 pages:191-207
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In this process, it is also very important to develop sophisticated forecast techniques for steel production in order to determine the real growth rate of an economy. Especially in South Asia where steel and its production, is one of the largest segment of the economy. Majority population of this region has associated directly or indirectly with steel industry. Inspite the importance of steel for this region, no conclusive research work has done related to this issue. This study investigates the forecasts of steel production in Pakistan for the first time, which is an important emerging economy of the South Asian region. Prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The auto regressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model.This paper forecasts steel production by using different time series ARIMA models. A data set of steel production of Pakistan from 1972 -2010 is used for the analysis. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (1,1,4) is suitable model for prediction of steel production in this case. It is concluded that model- 3 of this study is the best model to forecast the production of steel in Pakistan. After checking all the test it is come to know that the data is stationary at first difference and AR(1), MA(1), MA(2), MA(3) and MA(4) with first order is suitable for forecasting the production of steel. The forecasted values obtained from model- 3 are closer to the actual values as compared to the other model. The forecasted value of 2014 show that the production of steel in that particular year appeared to be least as compared to the production during the last five years. These results suggest that the policy makers and planning division of the country must give attention toward this matter and try to find out the reasons of low productivity of steel in the country. 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author	Ikram, Amir
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title_auth	Time series modelling for steel production
abstract	Steel and its production has now become an important ingredient for the economy of emerging countries. In this process, it is also very important to develop sophisticated forecast techniques for steel production in order to determine the real growth rate of an economy. Especially in South Asia where steel and its production, is one of the largest segment of the economy. Majority population of this region has associated directly or indirectly with steel industry. Inspite the importance of steel for this region, no conclusive research work has done related to this issue. This study investigates the forecasts of steel production in Pakistan for the first time, which is an important emerging economy of the South Asian region. Prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The auto regressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model.This paper forecasts steel production by using different time series ARIMA models. A data set of steel production of Pakistan from 1972 -2010 is used for the analysis. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (1,1,4) is suitable model for prediction of steel production in this case. It is concluded that model- 3 of this study is the best model to forecast the production of steel in Pakistan. After checking all the test it is come to know that the data is stationary at first difference and AR(1), MA(1), MA(2), MA(3) and MA(4) with first order is suitable for forecasting the production of steel. The forecasted values obtained from model- 3 are closer to the actual values as compared to the other model. The forecasted value of 2014 show that the production of steel in that particular year appeared to be least as compared to the production during the last five years. These results suggest that the policy makers and planning division of the country must give attention toward this matter and try to find out the reasons of low productivity of steel in the country. It is also an opportunity for the policy makers to develop policies which may help the steel production in future.
abstractGer	Steel and its production has now become an important ingredient for the economy of emerging countries. In this process, it is also very important to develop sophisticated forecast techniques for steel production in order to determine the real growth rate of an economy. Especially in South Asia where steel and its production, is one of the largest segment of the economy. Majority population of this region has associated directly or indirectly with steel industry. Inspite the importance of steel for this region, no conclusive research work has done related to this issue. This study investigates the forecasts of steel production in Pakistan for the first time, which is an important emerging economy of the South Asian region. Prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The auto regressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model.This paper forecasts steel production by using different time series ARIMA models. A data set of steel production of Pakistan from 1972 -2010 is used for the analysis. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (1,1,4) is suitable model for prediction of steel production in this case. It is concluded that model- 3 of this study is the best model to forecast the production of steel in Pakistan. After checking all the test it is come to know that the data is stationary at first difference and AR(1), MA(1), MA(2), MA(3) and MA(4) with first order is suitable for forecasting the production of steel. The forecasted values obtained from model- 3 are closer to the actual values as compared to the other model. The forecasted value of 2014 show that the production of steel in that particular year appeared to be least as compared to the production during the last five years. These results suggest that the policy makers and planning division of the country must give attention toward this matter and try to find out the reasons of low productivity of steel in the country. It is also an opportunity for the policy makers to develop policies which may help the steel production in future.
abstract_unstemmed	Steel and its production has now become an important ingredient for the economy of emerging countries. In this process, it is also very important to develop sophisticated forecast techniques for steel production in order to determine the real growth rate of an economy. Especially in South Asia where steel and its production, is one of the largest segment of the economy. Majority population of this region has associated directly or indirectly with steel industry. Inspite the importance of steel for this region, no conclusive research work has done related to this issue. This study investigates the forecasts of steel production in Pakistan for the first time, which is an important emerging economy of the South Asian region. Prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The auto regressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model.This paper forecasts steel production by using different time series ARIMA models. A data set of steel production of Pakistan from 1972 -2010 is used for the analysis. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (1,1,4) is suitable model for prediction of steel production in this case. It is concluded that model- 3 of this study is the best model to forecast the production of steel in Pakistan. After checking all the test it is come to know that the data is stationary at first difference and AR(1), MA(1), MA(2), MA(3) and MA(4) with first order is suitable for forecasting the production of steel. The forecasted values obtained from model- 3 are closer to the actual values as compared to the other model. The forecasted value of 2014 show that the production of steel in that particular year appeared to be least as compared to the production during the last five years. These results suggest that the policy makers and planning division of the country must give attention toward this matter and try to find out the reasons of low productivity of steel in the country. It is also an opportunity for the policy makers to develop policies which may help the steel production in future.
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