International Journal of Statistics and Reliability Engineering

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Vol 10, No 2:

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Modeling and Forecasting the Gross Domestic Product of Nepal Using Autoregressive Integrated Moving Average (ARIMA) Models

Abstract

Gross Domestic Product (GDP), often referred to as an economy's heartbeat, is defined as the total monetary or market value of all the finished goods and services produced within the geographic boundaries of the nation over the course of a specified period. Based on the World Bank data for the period 1960- 2021, we propose univariate Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) model for predicting the GDP data of Nepal. In the present paper, empirical analysis conducted by using software EViews finds ARIMA (0, 1, 2) to be the best fit model for forecasting GDP of Nepal with the forecast horizon of ten years, presuming existence of similar market conditions, with AIC of -1.862510 and SIC of -1.758696. The forecasted log transformed GDP values for the years 2022 and 2031 were respectively 3.691138 billion USD and 4.327831 billion USD. This implies that the Nepalese economy is steadily tending towards economic growth. The proposed ARIMA (0, 1, 2) model employs a difference operator of order one to integrate moving average of second order lag with a zero lag order for autoregression. This study specifically aims at economic forecasting and predictive analysis for GDP of Nepal. Out of sample one step ahead prediction for the year 2022 is in sync with the now available World Bank Report 2022.

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ISSN(P) 2350-0174

ISSN(O) 2456-2378

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