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      Box-Jenkins ARIMA Modelling: Forecasting FDI in India

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            Summary

            This research study meticulously focused on utilizing stastistical time series ARIMA modelling technique using Python for forecasting the FDI in India in forthcoming years. It proposed an ARIMA(1,1,4) model which shows an accuracy of 96.5% prediction when tested on sample data from 2011-2019.

            The dataset and code of this research study is provided along with the article.

            Abstract

            In the rapidly advancing dynamics of the economy trends of countries, the forecasting econometric techniques hold significant importance in the field of advance economics and management. Thus, this study intends to create Box Jenkins time series ARIMA model for analysing and predicting the trend of net FDI (Foreign Direct Investment) in India. The model was generated on the dataset of FDI inflow of India from the year 1950 to 2020. The trend was analysed for the generation of the model that best fitted the forecasting. The study highlights the minimum AIC value and involves ADF test (Augmented Dickey-Fuller) to transform FDI data into stationary form for model generation. It proposes ARIMA (1,1,4) model for optimal forecasting of net FDI inflow in India with an accuracy of 96.5%. The model thus predicts the steady-state exponential growth of FDI inflow in the coming 2020-25.

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            Author and article information

            Journal
            ScienceOpen Posters
            ScienceOpen
            10 November 2020
            Affiliations
            [1 ] Department of Computer Science & Engineering, ASET, Guru Gobind Singh Indraprastha University, New Delhi, India
            Author information
            https://orcid.org/0000-0003-1012-1407
            https://orcid.org/0000-0002-1777-3240
            Article
            10.14293/S2199-1006.1.SOR-.PPZCKJY.v1
            ab231b21-9ea0-4731-8aa4-c480b0d6d7b2

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            History
            : 10 November 2020

            All data generated or analysed during this study are included in this published article (and its supplementary information files).
            Computer science,Statistics,Engineering,Mathematics,Economics
            Moving Average,ARIMA,FDI,Time Series Forecasting,ADF-Test,Machine Learning,India,Auto-Correlation,Partial Auto- Correlation,Akaike's Information Criterion

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