Estimation of a differencing parameter of Auto-Regressive Integrated Moving Average models

Joint Event on 7^th International Conference on Biostatistics and Bioinformatics & 7^th International Conference on Big Data Analytics & Data Mining

September 26-27, 2018 | Chicago, USA

Abdul Basit, Anam Riaz, Zafar Iqbal and Munir Ahmad

State Bank of Pakistan, Pakistan
National College of Business Administration & Economics, Pakistan
Government Post Graduate College S/Town Gujranwala, Pakistan

Scientific Tracks Abstracts: J Comput Sci Syst Biol

Abstract :

Forecasting of key economic indicators has an important role in the policymaking. Statisticians and economist are still trying to find out the techniques and models which provides a more accurate forecast. There are different time series models are available in the literature like Auto-Regressive (AR) model, Moving Average (MA) model, Auto-Regressive Moving Average (ARMA) model, Auto-Regressive Integrated Moving Average (ARIMA) model, Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) model and many others. ARIMA and ARFIMA mostly used for the analysis of time series. In this study, we are trying to estimate the differencing parameterâ?? using the information function and entropy. The comparison of classical time series models and a new time series model is also included in this study. The new estimator of the differencing parameter will give us a more accurate forecast as compared to the classical time series models.

Biography :

Abdul Basit is the PhD Research Scholar in the discipline of Statistics in National College of Business Administration and Economics Lahore, Pakistan. He has completed his MS in Social Sciences from SZABIST Karachi, Pakistan in 2014. Currently, he is serving as Deputy Director in Research Cluster of State Bank of Pakistan. He has published 07 research papers in journals and many articles were presented at national and international conferences.

E-mail: abdul.basit2@sbp.org.pk