Modeling Consumer Price Index: An Empirical Analysis Using Expert Modeler

  • Chaitanya Singla Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
  • Pradeepta Kumar Sarangi Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
  • Sunny Singh Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
  • Ashok Kumar Sahoo Graphic Era Hill University, Dehradun, India
Keywords: Consumer Price Index, Time Series Forecasting, Expert Modeler, ARIMA Model

Abstract

Consumer price index (CPI) a popular economic indicator for India that represents the prices paid by customers for goods and services consumedby them.CPI is often used as an economic indicator that reflects the change in prices of goods and services over a period of time. In this work an attempt has been made to develop a forecasting model for India’s CPI for the period of May to December 2018. The data used in this work is the all-India CPI data for the period January 2013 - April 2018. SPSS Expert Modeler method has been used to fit the models and analyzing the data.

Downloads

Download data is not yet available.

References

[1] Singh, N., Sarangi, P.K., Chauhan, R.K., & Singh, R. 2009. Short term load forecasting using artificial neural network: A comparison with Genetic Algorithm implementation. Journal of Engineering and Applied Sciences 4 (9): 88-93.
[2] Sarangi, P.K., Singh, N., Chauhan, R.K., & Singh, R. 2009. Short term load forecasting using neuro genetic hybrid approach: Results analysis with different network architectures.Journal of Theoretical and Applied Information Technology 5(8):109-116.
[3] Sarangi, P.K., &Sarangi, P. 2010. Load forecasting using artificial neural network: Performance evaluation with different numbers of hidden neurons. IUP Journal of Information Technology 6 (1): 34-42.
[4] Sarangi, P.K., &Sarangi, P. 2010. Short-Term Load Forecasting Using Neural Network Technology. IUP Journal of Computer Sciences 4 (2): 15-23.
[5] Gupta, A.,Sarangi, P.K., & 2012. Electrical load forecasting using Genetic Algorithm based Back-propagation method. ARPN Journal of Engineering and Applied Sciences 7(8):1017-1020.
[6] Singh, S&Sarangi, P.K., 2014. Growth rate of Indian spices exports: Past trend and future prospects. Apeejay - Journal of Management Sciences and Technology 2 (1): 29-34.
[7] Sarangi, P.K.,Bano, S., &Pant, M. Future trend in Indian automobile industry: A statistical approach. Apeejay-Journal of Management Sciences and Technology 1(2): 28-32.
[8] Sarangi, P. K. 2010, Innovations in Management Science. Global Research Publications: 283-298, edited by Dr.Prasant Sarangi, ISSN/ISBN No. 8189630075, 9788189630072.
[9] Sinha, D,Sarangi, P., Sinha, S., & Sharma, M. (2018). Forecasting consumerprice indexusing neuralnetworkmodels. 3rd Conference on Innovative Practices in Operations Management & Information technology. Published in book of proceedings, ISBN 978-93-84562-11-3: 84-93.
[10] Norbert, H., Wanjoya, A., &Waititu,A. 2016. American Journal of Theoretical and Applied Statistics 2016; 5(3): 101-107 http://www.sciencepublishinggroup.com/j/ajtas doi: 10.11648/j.ajtas.20160503.14 ISSN: 2326-8999 (Print); ISSN: 2326-9006 (Online), pp-101-107.
[11] Cryer J.D., & Chan, K.S. 2008. Time Series Analysis with Application in R. New York, Springer.
[12] Brocwell,P.J., & Davis,R.A. 2002. Introduction to time series and Forecasting. New York: Springer.
[13] Box,G.E.P., & Jenkins,G.M. 1976. Time series Analysis: Forecasting and Control. San Fransisco: Holden-Day.
[14] Montgomery, D.C., Jennings,C.L., &Kulahci,M. 2008. Introduction to Time Series Analysis and Forecasting. New York: John Wiley& Sons.
[15] https://tradingeconomics.com/india/consumer-price-index-cpi.
[16] Iqbal, M., &Naveed, A.2016. Forecasting Inflation: Autoregressive Integrated Moving Average Model.European Scientific Journal12(1): 83-91.
[17] Dongdong, W. 2010. The Consumer Price Index Forecast Based on ARIMA Model.WASE International Conference on Information Engineering. DOI: 10.1109/ICIE.2010.79, Publisher: IEEE.
[18] Adams, S.O., Awujola,A., &Alumgudu,A.I. 2014. Modeling Nigeria’s Consumer Price Index Using Arima Model.International Journal of Development and Economic Sustainability 2(2): 37-47.
[19] He, Q., Shen, H., &Tong,Z. 2012. Investigation of Inflation Forecasting. Applied Mathematics & Information Sciences An International Journal6(3): 649-655.
[20] Aiken, M. 1999. Using a neural network to forecast inflation.Journal of Industrial Management & Data Systems 99(7): 296–301.
[21] Salzano, M., & Colander, D. 2007. Complexity Hints for Economic Policy. Milan: Springer
[22] Choudhary, A., & Haider, A. 2011. Neural Network Models for Inflation Forecasting: an Appraisal. Applied Economics 44(20) DOI: 10.1080/00036846.2011.566190
[23] McAdam, P., & McNelis, P. 2005. Forecasting Inflation with Thick Models and Neural Networks.Economic Modeling 22(5): 848–867
Published
2019-04-01
How to Cite
Chaitanya Singla, Pradeepta Kumar Sarangi, Sunny Singh, & Ashok Kumar Sahoo. (2019). Modeling Consumer Price Index: An Empirical Analysis Using Expert Modeler. Journal of Technology Management for Growing Economies, 10(1), 43-50. Retrieved from https://tmg.chitkara.edu.in/index.php/tmge/article/view/139
Section
Articles