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.

Financial well-being has significantly more importance in life than it used to be a few decades back, as more and more people face financial distress and financial problems. Concerning the importance of financial well-being, this study intends to unearth the impact of financial socialization and behavior on young adults' financial well-being. The study population was young individuals aged 18-29 years, and the responses were collected through an online questionnaire using Google Forms. The study adopted the quantitative approach, for which the data from 391 respondents was collected. Subsequently, the data was analyzed with the help of Smart-PLS, where reliability and validity were established, and the hypothesis and relation were tested through path analysis. The result indicated that financial socialization and financial behavior significantly impact young adults' financial well-being. However, financial behavior's impact was more significant than financial socialization. Thus, to increase the financial well-being of young adults, different educational programs that will positively impact financial socialization and behavior should be implemented, which may lead to financial stability and a strong financial base.

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 investigates the impact of governance quality, political stability, corruption management, regulatory quality, and governmental effectiveness on economic growth in Nepal, utilizing annual time series data spanning from 1996 to 2022. Advanced econometric techniques, including Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR), are employed to analyze both short- and long-term relationships. The results reveal a significant positive relationship between political stability and economic growth (FMOLS coefficient = 5.51E+09, p < 0.01), underscoring the importance of a stable political environment. Conversely, government effectiveness negatively correlates with GDP (FMOLS coefficient = -2.31E+10, p < 0.01), suggesting potential inefficiencies in governance. Corruption control exhibits mixed effects, showing a positive influence on GDP in some models (CCR coefficient = 1.11E+10, p < 0.10). Similarly, regulatory quality yields variable results, with a positive DOLS coefficient of 9.68E+09 (p < 0.05). Johansen's cointegration test confirms significant long-term relationships among the variables, while Granger causality tests identify political stability and government effectiveness as key drivers of GDP. These findings highlight the need for governance reforms that focus on enhancing political stability, improving regulatory quality, and addressing inefficiencies in public administration. Policymakers are urged to implement targeted strategies to promote sustainable economic growth in Nepal.

The advancement of financial technologies has led customers to use digital transactions more and more with the passage of time. With this issue, the share of physical transactions based on cash and coins is decreasing, and more and more people are moving away from traditional payment methods to digital transaction methods. Thus, this study tends to foreground the factors impacting customer’s intention to use digital transactions. This research foregrounds on grasp the control of subjective norms, trust, behavior control, and attitude on the customer’s digital transaction intention. The population of the study was made up of customers and people using electronic transactions on a daily basis. The study adopted a quantitative approach, for which four hundred and four responses were collected through the online questionnaire, and the responses were analyzed using PLS-SEM model approach with Smart PLS 4.0. The exploration of the data concludes that there is a critical positive relation between attitude, perceived behavior control, and usage intention, whereas no significant relation among subjective norms, trust, and usage intention for digital transactions. The implications of this study would benefit to the businessmen, customers, industrialists and organizational leaders to understand the motivation of customers in the trend of digital transaction.

This study examines the environmental effects of remittances in the five largest remittance-receiving countries (India, Mexico, China, the Philippines, and Pakistan) using panel data from 1990 to 2022 sourced from the World Development Indicators. The study employed a quantitative and analytical research design. Remittances are a critical component of economic stability in these countries, yet their impact on carbon emissions and environmental sustainability remains underexplored. The study utilized a dynamic ordinary least squares (DOLS) method to analyze study variables. Unit root and cointegration tests were performed to assess long-run relationships. A dynamic ordinary least squares (DOLS) (pooled estimation) results revealed that GDP per capita and trade openness have significant positive influences on CO₂ emissions. On the contrary, urban population has significant negative influences on CO₂ emissions. In contrast, remittances show no notable effect on CO2 emissions. Furthermore, the results show significant long-run cointegration among the variables, with GDP per capita, trade openness, and urban population identified as major drivers of CO2 emissions. These findings indicate that economic growth, trade liberalization, and demographic expansion are key drivers of environmental degradation, while the direct environmental impact of remittances appears minimal. The study recommends policymakers prioritize environmentally sustainable investments to align remittance-driven economic growth with global climate goals.