2020Unpublished venueOpen access

Box-Jenkins ARIMA Modelling: Forecasting FDI in India

Deepanshu Sharma, Kritika Phulli

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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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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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Available 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.

Key concepts: Autoregressive integrated moving average, Foreign direct investment, Inflow, Box–Jenkins, Econometrics, Econometric model, Economics, Time series

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