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Asymmetric Effects of Economic Policy Uncertainty on the Demand for Money in India

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Abstract

This study aims to extend the conventional money demand function by including the economic policy uncertainty (EPU) index in the Indian money demand function. The rest of the determinants are income, interest rate, inflation rate, and exchange rate. Both symmetric and asymmetric effects of uncertainty are estimated covering the period 2003M1–2018M4. The linear ARDL bounds testing approach shows that uncertainty has a significant effect on narrow money in the short run. At the same time, the asymmetric nonlinear framework supports the short-run asymmetric effect of uncertainty on both narrow and broad money. More precisely, the policy uncertainty is a short-run phenomenon for the Indian money demand function. However, both linear and nonlinear models yield a stable demand for money in India regardless of narrow money or broad money. Hence, the monetary policy can be initiated to tune the Indian economy.

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Notes

  1. Due to unstable money demand function, The Economist raised a question in 1986, “Is this the year monetarism vanishes?”.

  2. Baker et al. (2016) developed the index of economic policy uncertainty after considering the three terms of (1) economy, (2) policy, and (3) uncertainty together. They mostly relied on newspaper articles. Besides, they discussed with people who have expertise in the relevant economy. In the case of the policy term, they customized the set of variables across countries where needed. For more details about this index visit the website: http://www.policyuncertainty.com/index.html.

  3. For instances, Gujarati (1968) estimated the demand for money considering annual data from 1948 to 1964. The author found that significant income elasticity in the short-run and long-run. On the contrary, He did not find any significant interest rate elasticity effect on Indian money demand. Rao and Singh (2006) estimated a long time series over the period 1953–2003 and found that the demand for narrow money is significant in India. Das and Mandal (2000) found that a stable demand for M3 using partial adjustment and distributed lag models. However, Inoue and Hamori (2009) found that the stable money demand function for M1 and M2, while the demand for M3 is unstable. Haider et al. (2017) found insignificant exchange rate effect on demand for money while it becomes significant in the asymmetric framework.

  4. More specifically, Baumol (1952) analyzed the velocity of transactions at a constant interest rate, while Tobin (1956) estimated the interest rate elasticity of holding money balance at a constant level of transactions.

  5. During the Bretton Woods system, Indian rupee followed the par value system of the IMF. Afterward, the Indian rupee was pegged with major currencies from 1971 to 1992. However, finally, India has been following a freely floating exchange rate system since 1993 (Pattnaik et al. 2003). The exchange rate effect on Indian money demand function is ambiguous. While Singh and Pandey (2010) did not find a significant effect of exchange rate on demand for holding money, Haider et al. (2017) found the short-run and long-run asymmetric effect of exchange rate on the Indian money demand function.

  6. For example, although Engle and Granger (1987), Stock and Watson (1988), Phillips and Ouliaris (1990), Johansen (1991, 1995), Shin (1994), Zhou and Lin (2018) employed different procedures in their cointegration tests, all of them concentrated on integrated of order one \(I(1)\) series.

  7. Facts behind peaks and troughs of this index can be found in Bhagat et al. (2013).

  8. In the absence of a deterministic trend, ERS unit root test of a demeaned series can be estimated in the following way:

    $$\Delta {y}_{t}^{d}={\alpha }_{0}{y}_{t-1}^{d}+\sum_{i=1}^{k}{\beta }_{i}{y}_{t-i}^{d}+{\varepsilon }_{t}$$

    where the demeaned series \({y}_{t}^{d}={y}_{t}-{\widehat{\beta }}_{0}\). The lags of \({y}_{t}^{d}\) are included for considering the serial correlation effect of residuals. In case of a deterministic trend, the detrended series \({y}_{t}^{\tau }\) of the original series \({y}_{t}\) would be \({y}_{t}^{\tau }={y}_{t}-{\widehat{\beta }}_{0}-{\widehat{\beta }}_{1}t\), and \({y}_{t}^{\tau }\) will be replaced in the above equation.

  9. Kapetanios and Shin (2008) proposed a GLS based nonlinear unit root tests against the alternative hypothesis of a globally stationary exponential smooth transition autoregressive (henceforth, ESTAR) process, referred to \({\stackrel{\sim }{\omega }}^{j}\), are given by the Wald tests for \({\phi }_{1}={\phi }_{2}=0\) in the following regression:

    $$\Delta {\tilde{y }}_{t}^{j}={\phi }_{1}{\tilde{y }}_{t-1}^{j}{1}_{\left\{{\tilde{y }}_{t-1}^{j}\le {r}_{1}\right\}}+{\phi }_{2}{\tilde{y }}_{t-1}^{j}{1}_{\left\{{\tilde{y }}_{t-1}^{j}>{r}_{2}\right\}}+\sum_{i=1}^{k}{\beta }_{i}\Delta {\tilde{y }}_{t-i}^{j}+{\varepsilon }_{t}$$

    where, \(j=\mu\) for demeaned series and, \(j=\tau\) for detrended series. \({r}_{1}\) and \({r}_{2}\) are the threshold parameters.

    Like ERS, the lags of \(\Delta {\tilde{y }}_{t}^{j}\) are considered to deal with serially correlated residuals.

  10. Haider et al. (2017) failed to find a significant effect of exchange rate on demand for \({M}_{1}\) and \({M}_{3}\) in the framework of linear ARDL model.

    Contrary to the linear approach, they found short-run and long-run asymmetric effects of exchange rate on demand for \({M}_{1}\) and \({M}_{3}\) after introducing nonlinear ARDL model. However, since the real effective exchange rate has been considered in this study, there may have some aggregation bias problems. The exchange rate effects can be further studied using the bilateral exchange rate of major trading partners of India.

  11. For instance, Bahmani-Oskooee and Nayeri (2018b) also found such conflicting outcome between short-run Wald test report and estimated coefficients of positive and negative partial sums. They found statistically significant positive and negative partial sum at each specific lag while the \({W}_{SR}\) statistic is not significant. Finally, they concluded that the different coefficient estimates of the partial sums supported the short-run asymmetry.

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Authors and Affiliations

  1. Department of Economics, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh

    S. M. Woahid Murad & Md. Golam Kibria

  2. School of Economics & Finance, Curtin Business School, Curtin University, Perth, WA, 6845, Australia

    Ruhul Salim

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Appendices

Appendix

Definition of Variables and Sources of Data

Monthly data covering the period 2003:M1–2018:M4 is collected from the following sources:

Variables

Definition

Source

\(lnnm\)

Narrow money

Narrow money includes transferable deposits and currency outside deposit money banks

(a)

\(lnbm\)

Broad money

Broad money is the summation of narrow money and quasi-money, where the quasi-money covers liabilities of financial institutions, comprising time, savings, and foreign currency deposits

(a)

\(lny\)

Industrial Production Index

Industrial production refers to the volume of output generated by production units classified under the industrial sectors

(a)

\(lnr\)

Interest rate

The estimated interest rate is the lending interest rate, which is the depository corporations' rate that generally satisfies the short- and medium-term financing needs of the private sector

(a)

\(lns\)

Real Effective Exchange Rate

Real effective exchange rates are obtained from the weighted averages of bilateral exchange rates of Indian rupees adjusted by relative consumer prices

(b)

\(ln\dot{P}\)

Inflation Rate

The inflation rate is estimated by the consumer price index (CPI)

(a)

\(EPU\)

Economic policy uncertainty

The EPU index is constructed from three components: (1) quantifies seven Indian newspaper coverage of policy-related economic uncertainty; (2) different sorts of policies taken by the government and the monetary authority; and finally, (3) dispute among economic forecasters as an alternative of uncertainty

(c)

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Murad, S.M.W., Salim, R. & Kibria, M.G. Asymmetric Effects of Economic Policy Uncertainty on the Demand for Money in India. J. Quant. Econ. 19, 451–470 (2021). https://doi.org/10.1007/s40953-021-00235-1

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