MODELING AND FORECASTING OF INDIA’S DEFENSE EXPENDITURES USING BOX-JENKINS ARIMA MODEL
Keywords:ARIMA Model, Defense Expenditure, Box-Jenkins, Forecasting, India
Many developed and developing countries are at the core of the security and peace agenda concerning rising defense expenditure and its enduring sustainability. The unremitting upsurge in defense expenditure pressurizes the government to rationally manage the resources so as to provide security and peace services in the most efficient, effective and equitable way. It is necessary to forecast the defense expenditure in India which leads the policy makers to execute reforms in order to detract burdens on these resources, as well as introduce appropriate plan strategies on the basis of rational decision making for the issues that may arise. The purpose of this study is to investigate the appropriate type of model based on the Box–Jenkins methodology to forecast defense expenditure in India. The present study applies the one-step ahead forecasting method for annual data over the period 1961 to 2020. The results show that ARIMA (1,1,1) model with static forecasting being the most appropriate to forecast the India’s defense expenditure.
Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis. Forecasting and control. San Francisco: Holden-Bay.
Church, Keith B., and Stephen P. Curram. (1996). Forecasting consumers' expenditure: A comparison between econometric and neural network models. International journal of forecasting 12(2), 255-267.
Dickey, D. A., & Fuller, W. A. (1979). Distributions of the estimators for autoregressive time series with a unit root. Journal of American Statistical Association, 74(366), 427–431.
Dickey, D. A., & Fuller,W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072. Koutroumanidis, Theodoros, Konstantinos Ioannou, and Garyfallos Arabatzis. (2009). Predicting fuel wood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA–ANN model. Energy Policy 37(9), 3627-3634.
Kuo, Kuo-Cheng, Chi-Ya Chang, and Wen-Cheng Lin. (2013). To predict military spending in China based on ARIMA and artificial neural networks models. Przegląd Elektrotechniczny 89(3b), 176-181.
Faruk, Durdu Ömer.(2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering applications of artificial intelligence, 23(4), 586-594.
Meyler, Aidan; Kenny, Geoff and Quinn, Terry (1998). Forecasting Irish inflation using ARIMA models, Central Bank and Financial Services Authority of Ireland Technical Paper Series, 1998 (3/RT/98), 1-48.
Peijun, C. (2016). Predictive Analysis of Chinese Total Health Expenditure Base on ARIMA Model. Medicine and Society, (03).
Phillips, P. C. B., & Perron, P. (1998). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.
Raymond Y.C. Tse (1997). An application of the ARIMA model to real-estate prices in Hong Kong, Journal of Property Finance, 8(2), 152 – 163.
Sharma Deepanshu & Kritika Phulli (2020 ). Forecasting and Analyzing the Military Expenditure of India Using Box-Jenkins ARIMA Model. arXiv:2011.06060 [econ.GN] General Economics (econ.GN).
SIPRI (2020). SIPRI Military Expenditure Database. Stockholm International Peace Research Institute (SIPRI) published by Oxford University Press. www.sipri.org/databases/milex.
Stergiou, K. I. (1989). Modeling and forecasting the fishery for pilchard (Sardina pilchardus) in Greek waters using ARIMA time-series models. ICES Journal of Marine Science, 46(1), 16-23.
Yue Z., W. Shengnan and L. Yuan (2015). Application of ARIMA Model on Predicting Monthly Hospital Admissions and Hospitalization Expenses for Respiratory Diseases. Chinese Journal of Health Statistics, (02), 197-200.
How to Cite
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.