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

Where Δ represents the first differences of the variables, ∑’s represents the error correction dynamic, and β ’s shows the long-run relationship, α 0 is the drift component and ε t is the white noise residuals. I analyzed the ARDL directly by using e-views 11. The null hypothesis that there is no co-integration among the variables against the alternative hypothesis of the existence of co- integration among the variables are given below: The value of F-statistics is compared with the tabulated values of Narayan (2004) for the small sample size (30 to 80 observations). If the F-statistics value is greater than the upper critical value, reject the null hypothesis that means there exists a co-integration relationship or long-run relationship among the variables. If the value of F-statistics is less than the lower critical value, accept the null hypothesis, which means there is no co-integration among the variables. If, however, the F-statistics value lies within the upper and lower bound, the results are inconclusive. We employ the Akaike Information Criteria (AIC) to determine the optimal lag length for the study. The ARDL restricted ECM models is defined as: (4) Where ψ is the speed of adjustment. ECM shows how much disequilibrium is being corrected (Emeka N., Kelvin U. (2016). A positive coefficient of ECM indicates a divergence, and a negative coefficient reveals a convergence. If the estimate of ECM = 1, 100% of the adjustment takes place each period that is the adjustment is instantaneous and full; if the estimate of ECM = 0.5, 50% of the adjustment is realized each period/year. ECM = 0 indicates that there is no adjustment, and no longer makes sense of the long- term relationship. At last, to ensure the goodness of fit of the selected model author conduct the stability and diagnostic test. To check the stability of the parameters of the model, the author employs the stability test of the cumulative sum (CUSUM) of recursive residuals proposed by Pesaran and Pesaran (1997) and the cumulative sum of squares (CUSUMSQ) of recursive residuals by Brown et al. (1975). V. R esult a) Unit Root Test To check the stationary of the variables, researchers used the Augmented Dickey-Fuller (ADF) test. The result of the ADF test is given in table 1. Table 1: ADF and PP test result ADF Variable At level 1 st difference Order of integration Constant Constant, linear trend Constant Constant, linear trend LNGS -6.76*** -8.63*** -- -- I(0) LNINT -2.51 -3.53** -4.41*** -4.54*** I(1) LNINF -5.87*** -5.69*** -- -- I(0) LNREM -4.92*** -2.72 -7.77*** -7.54*** I(1) LNGDP 5.93 1.28 -4.51*** -7.63*** I(1) Significance at ***1%, **5%, and *10%. Table 1 shows the test of stationary result. From the table, we can see that LNGS and LNINF are stationary at level, and LNINT, LNREM and LNGDP are nonstationary at level but stationary at 1st difference. Since some variables are integrated I(0) and some are I(1), we estimate ARDL long- run and short-run estimates. b) Optimal Lag The result of the Akaike information criterion indicates that the optimal lag of the selected model is 4,2,3 4,4, shown in figure 1. Volume XXI Issue IV Version I 34 ( E ) Global Journal of Human Social Science - Year 2021 © 2021 Global Journals H0: β1= β2= β3= β4= β5=0 ΔLNGS t =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 + ψECM t-i +ε t An Empirical Analysis of Interest Rate and Domestic Savings in Bangladesh