Economic Review

ISSN No: 1608-6627

Editorial Board

Articles in this volume
[Nepal Rastra Bank Research Department]
Abstract

This paper estimates the optimal inflation rate in Nepal based on the data of the period 1978–2016. The novelty of the analysis is that it probes possible nonlinearity of the hypothesized impact of inflation on economic growth using alternative specifications. The results suggest that there exists a threshold effect of inflation. The Ordinary Least Squares method estimates the turning point of inflation to be 6.25 percent while that of the Hansen (2000) method shows the threshold level to be 6.40 percent. The maximum impact on growth associated with the turning point, and at the mean levels of other explanatory variables is quite high at 4.59 percent. The results suggest that Nepal should adopt an inflation target range around the computed optimal inflation rate to lower the inflation expectation and enhance economic growth.

[Nepal Rastra Bank Research Department]
Abstract

In this paper, we use auto-regressive distributed lag (ARDL) approach to co-integration developed by Pesaran et al. (1999) to estimate the elasticity and buoyancy coefficients of various revenue heads. We find that long-run buoyancy coefficients are greater than unity for all revenue heads except for custom duty whereas elasticity coefficients except for VAT are smaller than unity. Short-run buoyancy and elasticity coefficients for all revenue heads are found smaller than unity. We find OLS estimates of these coefficients to be spurious for the sample 1975-2016. These coefficients will be biased if data generating process (DGP) excludes tax exemption. All components of revenue besides income tax and VAT are found to be neutral to inflation. Empirical evidence suggests that custom reform should get top priority in the reform of revenue administration.

[Ramesh Prasad Chaulagain]
Abstract

Financial literacy is an emerging and common concept both of education and finance. In general, the concept is important for every ones who has to manage the money; the concept plays vital role for low income people and small borrowers in particular. The small borrowers are those who borrow a limited amount of money from the licensed financial institutions. The borrowers are small on the basis of their credit limits determined by Nepal Rastra Bank. In this study, financial literacy, as one of the significant factors to determine the financial behavior of small borrowers, was measured to establish the relationship to each other. In this paper, the level of financial literacy of small borrowers was compared with their financial attitude and behavior, for which data were collected from survey of small borrowers of two cooperatives licensed by Nepal Rastra Bank. Chi-square test was applied to test the hypothesis that showed the relationship between these selected variables. The analysis showed that the relationship of financial literacy of small borrowers was significant with their financial attitude and behavior. Hence, we argue that that there is a need of a systematic enhancement of financial literacy of the small borrowers to change their attitude and thereby the financial behavior.

[Hom Nath Gaire]
Abstract

In this study, an attempt has been made to demonstrate the usefulness of univariate time series analysis as both an analytical and forecasting tool for Nepali stock Market. The data set covers the daily closing value of NEPSE index for two and half years starting from the middle of 2012 to end 2015. The forecasting analysis indicates the usefulness of the developed model in explaining the variations, trend and fluctuations in the values of the price index of Nepali stock exchange. Explanation of the fit of the model is described using the Correlogram, Unit Root tests and ARCH tests, which finally confirm that the ARIMA and EGARCH are good in forecasting and predicting daily stock index of Nepal. Furthermore, it is inferred that the daily stock price index contains an autoregressive, seasonal and moving average components; hence, one can predict stock returns through the identified models.