Global Journal of Human Social Science, E: Economics, Volume 21 Issue 4

to collect information on saving rates. The study found that dissaving had been made at retirement age, even working. The findings were consistent with the life cycle model. Faridi et al. (2010) examined the determinants of household saving in the Multan district of Pakistan using the primary data. They concluded that spouse participation, total dependency rate, total income of household, and size of landholdings had a significant positive relationship with household saving. On the other hand, education household head, children’s educational expenditures, family size, liabilities to be paid, marital status, and value of the house have a significant negatively relationship with household saving. This study supports the existence of the Life cycle hypothesis. Wan et al. (2003) examined the determinants of rural China’s savings behavior. The result revealed that undeveloped capital market structure and absence of social rights have a negative impact on rural savings behavior. Aizenman, J. et al. 2017 investigated the interest rate effect on private saving: alternative perspectives using data on 135 countries from 1995 to 2014. The study showed that in an economy with a well-developed financial market, an aging population, and output volatility can all contribute towards turning the relationship between interest rates and saving negative. They detect the substitution effect of the real interest rate on private saving when the nominal interest rate is not too low among developing countries. The same results were detected among industrial and emerging economies only when the nominal interest rate is lower than 2.5%. In contrast, among emerging Asian countries, they found the income effect when the nominal interest rate is below 2.5%. III. D ata In this study, I used annual time-series data from the period 1976 to 2019. All data but remittance are collected from the world development indicators published by World Bank. The data of remittance is obtained from Bangladesh Bank. IV. M odel S pecification The model of the study takes the form: Gross Saving as a function of Gross Domestic Product, Deposit Interest Rate, Remittance, and Inflation rate. .........................................(1) Here, ε t is an error term which means there could be some other factors that can affect GS and β₀ is a scalar parameter, β₁ , β₂ , β₃ , and β 4 are the slope coefficient parameters. All variables are transformed into log-linear form (LN). According to Shahbaz et al. (2013), modeling the log-log model specification will provide efficient results by mitigating the sharpness in time series data than the simple linear-linear specification. …. (2) Here, LNGS= log of Gross Saving that measured in percentage of GDP. LNINT= log of Deposit interest rate. LNINF= log of Inflation Rate, LNREM= log of remittance measured by million USD, LNGDP = log of real domestic savings. β 0 = the constant term β 1 = Coefficient of variable LNINT, β₂ = coefficient of variable LNINF, β₃ = coefficient of variable LNREM, β 4= Coefficient of variable LNRGDP, t= the time trend. ε = the random error termMethodology. I employ the Autoregressive Distributed Lag (ARDL) bound test to estimate the short run and long- run dynamic relationship among the selected variables for the study. This test recommended by Pesaran and Shin (1999) and Pesaran et al.2001. The advantages of the ARDL method it provides more robust result in a small sample size (30 to 80 observations); this approach is not sensitive to orders of integration of the variables of interest, It is applicable if some variables are I(0) and some are I(1) ; it is based on a single equation framework; the ARDL bound testing approach to co- integration yields efficient simultaneous estimation and separation of the short- and long-run relationships between the variables of interest; moreover the ARDL Approach yields unbiased estimates and valid - statistics, even if some of the regressors are endogenous (Paul et al.2011, Benzamin et al. 2001, and Narayan, 2005) To employ this test, firstly we test the stationarity of the considered variables by using Augmented Dicked Fuller test (ADF) by Dickey and Fuller (1979, 1981) to see the order of integration. The ARDL is based on the assumption that the order of integrations of the variables are I(0) or I(1) (Ouattara, 2004). If any variables are integrated of I(2) , the results can be spurious, and the ADRL bound test is unsuitable (Pesaran & Shin, 1998). The equation for the ARDL test is as below: ..(3) Volume XXI Issue IV Version I 33 ( E ) Global Journal of Human Social Science - Year 2021 © 2021 Global Journals ΔLNGSt =α 0 + ∑ σ i Δ(LNGS) t-i + ∑ µ i Δ(LNINT) t-i + ∑φ i Δ(LNINF) t-i + ∑γ i Δ(LNREM) t-i + ∑η i Δ(LNGDP) t-i + β 1 LNGS t-1 +β 2 LNINT t-1 + β 3 LNINF t-1 + β 4 LNREM t-1 + β 5 LNGDP t-1 +ε t GS t = β 0 + β 1 INT t + β 2 INF t + β 3 REM t + β 4 GDP t + ε t LNGS t = β 0 + β 1 LNINTₜ+ β₂LNINFₜ + β₃LNREMₜ + β 4 LNGDPₜ+ εₜ …. …. …. …. An Empirical Analysis of Interest Rate and Domestic Savings in Bangladesh