Forecasting the Monthly Tourist Arrival from India to Nepal: An Econometrics Modeling Approach

Biplab Bhattacharjee (), Aayush Poudel (), Subin Panta (), Sashwat Sharma () and Samyak Pokharel ()
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Biplab Bhattacharjee: Jindal Global University
Aayush Poudel: Kathmandu University
Subin Panta: Kathmandu University
Sashwat Sharma: Kathmandu University
Samyak Pokharel: Kathmandu University

Chapter 8 in Leveraging Emerging Technologies and Analytics for Empowering Humanity, Vol. 1, 2025, pp 157-172 from Springer

Abstract: Abstract Tourism is a crucial component of the economy in developing countries like Nepal. Nepal is a popular tourist destination for India due to its proximity, shared cultural heritage, and affinities between the people and their respective religions. This study attempts to develop an econometric forecasting model to predict the monthly arrivals from India to Nepal. The COVID-19 pandemic significantly impacted the travel and tourism industry, causing a structural break in the time series arrival data. Using monthly data from 2004 to 2034, the study applies time series models to address complexities such as seasonality, non-stationarity, and structural breaks (due to COVID-19). The findings reveal that an ARIMAX model incorporating Google search trends data performs better than traditional models based on several evaluative measures such as RMSE, MAPE, AIC, and Theil’s U. The proposed forecasting model can assist policymakers, hotel management, and event planners in estimating the level of tourism demand and making better managerial decisions.

Keywords: Academic recruitment; Academic research performance; Predictive modeling (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-981-96-2548-2_8

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