Daya Simkhada

Wheat production in Nepal is highly sensitive to climatic variability, particularly temperature and rainfall. This study examines the asymmetric impacts of these climatic factors on wheat yields using the Nonlinear Autoregressive Distributed Lag (NARDL) model. A quantitative research design was employed, analyzing time-series data from 1990 to 2022. The NARDL model was used to capture both short- and long-term asymmetric effects of temperature and rainfall on wheat yield. Diagnostic and stability tests ensured the robustness of the model. The findings reveal that positive temperature changes significantly reduce wheat yields in the short term (coefficient: -2483.80, p < 0.01), followed by a compensatory positive effect in subsequent periods (coefficient: 2935.31, p < 0.01). Negative temperature changes and rainfall variability, both positive and negative, were statistically insignificant in their impacts. The model’s R-squared value of 0.972 highlights its strong explanatory power. The results underscore the critical role of temperature in influencing wheat productivity, with rainfall’s limited impact suggesting effective water management practices. These findings align with global studies on climate resilience but highlight unique adaptation mechanisms in Nepal. This study emphasizes the need for heat-tolerant wheat varieties and enhanced irrigation systems to mitigate climatic stressors. The use of the NARDL model offers novel insights into the asymmetric dynamics of climatic factors, advancing methodologies in climate-agriculture research.

Potato production in Nepal is essential to its agricultural economy, yet is vulnerable to climatic variability and inefficient practices. This study utilized a Dynamic Ordinary Least Squares (DOLS) approach to analyze data from 1990 to 2022, incorporating variables such as temperature, rainfall, and pesticides. Data were sourced from the FAO and Climate Change Knowledge Portal. Temperature exhibited a significant positive impact on potato yield (β = 35707.52, p < 0.001), whereas rainfall’s effect was statistically insignificant (β = -28.65, p > 0.05). Pesticide use showed a strong positive correlation (β = 81.91, p < 0.001), highlighting its critical role. Nutrient management strategies, such as NPK fertilizers, were identified as effective in enhancing yields, particularly in high-altitude regions. The findings reveal the interplay of climatic factors and agronomic practices in shaping potato productivity. The dependency on pesticides raises environmental concerns, while insufficient rainfall adaptation underscores the need for supplementary irrigation systems. Region-specific, adaptive strategies such as precision irrigation and integrated pest management are recommended. Integrating climatic and agronomic factors into policymaking is crucial for enhancing potato production in Nepal. Sustainable farming practices and infrastructural support are key to ensuring economic resilience and food security.

Nepal, a diverse and geographically varied landlocked country with a rich tapestry of cultures, and natural heritages, boasts iconic attractions such as Mt. Everest as the world’s highest peak, Lumbini as the birthplace of Lord Buddha, Pashupatinath Temple as one of the world’s most celebrated pilgrimages for the Hindus, Chitwan National Park, home to the one-horned rhino enlisted by United Nations Educational, Scientific and Cultural Organization world’s natural treasure, etc. Attracting tourists from all corners of the globe, Nepal’s unique blend of landscapes and cultural heritage has made this country a prominent destination. This research delves into a comprehensive trend analysis of tourist arrivals in Nepal from 1964 to 2023, employing the Auto Regressive Integrated Moving Average (ARIMA) model based on the secondary data employed from the Ministry of Finance, Government of Nepal. The model provides insights into past trends and forecasts future tourist arrivals, evidenced by the rigorous test using the Root Mean Square Error. The analysis employed several ARIMA models, including ARIMA (2, 1, 3), ARIMA (2, 1, 2), ARIMA (3, 1, 3), and ARIMA (3, 1, 2), with model performance assessed using criteria such as Akaike Information Criterion (AIC) and Mean Absolute Error (MAE). Findings indicate that the ARIMA (2 1, 3) model provides the most accurate forecasts compared to the other models tested. This finding has significant implications for Nepal’s tourism industry. The results underscore the importance of adopting flexible and strategic planning approaches to enhance the resilience of Nepal’s tourism sector. The study’s insights are valuable for policymakers, industry stakeholders, and researchers, contributing to the development of targeted strategies that ensure sustainable growth in the face of ongoing volatility.

This research aims to determine an Auto-Regressive Integrated Moving Average (ARIMA) model and forecasting the National Consumer Price Index (NCPI) in Nepal, using annual data from the fiscal year 1972/73 to 2022/23. The research utilized secondary data collected from the online bulletin published by Nepal Rastra Bank, aiming to enhance understanding of NCPI patterns and contribute to predictive modelling techniques for economic indicators. Adopting the Box-Jenkins technique and using E-Views statistical software, this study identifies ARIMA (1, 2, 8) as the most suitable model for NCPI forecasting. This selection is validated through meticulous diagnostic tests, affirming the appropriateness of the model, as evidenced by residuals exhibiting white noise characteristics. The research findings suggest a rapid increase in the National Consumer Price Index for the coming years. Implications of this research extend to both academic and policy-oriented audiences. A successful ARIMA model development holds significant potential for providing accurate predictions of future price trends in Nepal. This, in turn, can assist policymakers in making informed decisions regarding economic strategies, monetary policies, and inflation control measures. Moreover, the study's outcomes offer a foundational framework for future research endeavours in the domain of economic forecasting and modelling, contributing to the broader field of economic analysis and policy formulation.

This study examines the determinants of private consumption in Nepal, focusing on remittances, GDP, and inflation. Private consumption plays a critical role in economic growth, yet its drivers in Nepal remain underexplored, particularly concerning cross-border inflation impacts. Utilizing an ARDL framework, the study analyzes time-series data from 1975–2023. Unit root and F-Bounds tests were employed to ensure stationarity and confirm long-run relationships among variables. The Error Correction Model (ECM) captured short-run dynamics and adjustment speeds. Diagnostic tests ensured model robustness and stability. The findings reveal significant positive long-run effects of remittances (coefficient: 0.0256) and GDP (coefficient: 0.0433) on private consumption. Inflation exhibits mixed impacts, with Indian inflation negatively affecting consumption. The ECM demonstrated a rapid adjustment speed (−1.0095), indicating strong reversion to equilibrium. Diagnostic tests confirmed model stability and robustness. The results align with Keynesian theory, emphasizing income’s role in consumption, and highlight the novelty of incorporating cross-border inflation dynamics. The findings emphasize the need for policies stabilizing remittance inflows, promoting inclusive growth, and managing inflation. This study offers novel insights into the interconnectedness of macroeconomic variables and provides a framework for future research on consumption behavior in Nepal.