Asymmetric Effect of Fiscal Deficit on Current Account Deficit: Evidence from India

Abstract

This study has attempted to re-investigate the impact of fiscal deficit (FD) on current account deficit (CAD) (also known as twin deficit hypothesis) in India from 1970–1971 to 2018–2019 in the presence of private saving–investment gap (SI) and exchange rate (EXR). For the empirical investigation, the study has employed the nonlinear autoregressive distributed lag (NARDL) approach to cointegration. The NARDL results found the evidence of an asymmetric effect of FD, SI and EXR on CAD in the long run only. The obtained results support the traditional views of the Keynesian approach that FD has a positive impact on CAD, validates the existence of the ‘Twin Deficit Hypothesis’ in India. Further, results also depict that SI has a positive effect on CAD, whereas EXR has an adverse impact on CAD. From a policy standpoint, the asymmetric impact of FD on CAD provides strong reasons for conceiving policies that are adaptable to changing dynamics in internal as well as external sectors.

Modelling the role of fiscal and monetary policy instruments on carbon emission in non‐linear framework: A case of emerging economy

“…Moreover, we have used the general to a specific method in a similar fashion to Adil et al. (2020) Sharma and Mittal (2019), Vishal and Ashok (2021), Sharma et al (2021), Khan et al (2022) and Asif et al (2023) to estimate the short‐run and long‐run relationships from the dynamic models shown in Equations () and () and the results are reported in Table 8. In Table 8, Panel A exhibits the short‐run estimates, Panel B summarizes the long‐run estimates and Panel C provides the diagnostic tests for both models.…”

“…If the value of the F‐statistics is larger than the upper bound critical value, it states the existence of a long‐run relationship between the variables, but if the value is below the lower bound, it negates any relationship. The test is inconclusive if the value of F ‐statistics lies between the two bounds (Sharma et al., 2021).…”

Do oil prices have an asymmetric impact on economic output in India? Empirical evidence using asymmetric cointegration approach

“…For detecting nonlinear cointegration, the joint null hypothesis , representing no cointegration, is verified by adopting the bounds test, which relies on F ‐statistics. There are three cases: (i) in the instance that the F ‐statistic value surpasses the critical value limit, it can be inferred that there exists a long run association between the variables, (ii) if the value of F ‐statistics goes below the lower limit, the association is rejected and (iii) if F ‐statistics falls in the middle of two critical values, the test is ambiguous (Khan et al, 2022; Sharma et al, 2021).…”

“…Table 1 depicts that the response in real GDP due to an upswing in oil prices may be dissimilar compared to a downswing. Therefore, modelling macroeconomic behaviour in a linear framework provides misleading results when the macroeconomic variables' response is supposed to be nonlinear (Asif et al, 2023;Khan et al, 2022;Sharma et al, 2021Sharma et al, , 2023Sharma & Mittal, 2019;Vishal & Ashok, 2021). To overcome this issue, the present study utilises the nonlinear autoregressive distributed lag (NARDL) model put forth by Shin et al (2014).…”

Modeling the dynamics of oil and agricultural commodity price nexus in linear and nonlinear frameworks: A case of emerging economy

“…1 1 For estimating the ARDL and NARDL approaches, we have followed the general to specific procedure (see inter alia, Ajaz et al, 2016; Sharma et al, 2021). The desired specification is selected by initiating with max p = max q = 12 and removing all insignificant lags. …”