Abstract

This study investigates the causal relations among money, interest rate, and key macroeconomic variables, based on annual data from 1980 to 2019 by using the Granger causality test. In this study, we have constructed Divisia Superlative weighted monetary Index for Pakistan’s economy and investigated its causality with entire macroeconomic indicators. The outcomes of this research reveal that before financial innovation interest rate causes better compared to simple sum and superlative Divisia Sum, while, post reforms the causal performance of Superlative Divisia Sum is better compared to Simple Sum and rate of interest. Furthermore, the combined effect of both pre and post-reform Divisia Sum shows the best causality performance with key macroeconomic indicators as compared to its counterpart in case of macroeconomic stability. Policy implications that come out from this study that regulatory authorities may use these monetary aggregates (Divisia) targeting, which make a strong association among the money and key macroeconomic variables for sustainable development goals and best monetary policy implication. Therefore, it is concluded that, after financial innovation in Pakistan’s role of divisia monetary aggregate shows strong relation with economic variables. Moreover, superlative monetary aggregates show better results in case of predictability, information content, and policy regime change. The divisia monetary aggregate is also supportive, which provides accurate information to policymakers about the movement of monetary policy shocks observers due to expansionary and constructional monetary policy shocks. So, properly measurable, monetary aggregates never lose their capability to describe fluctuation on aggregate bases, which indicates an important omission from standard model and best policy discussion. So, the latest policy adopts to increase the money growth instead of the conventional once (simple sum). This school of thought is very much supported by evidence, that properly measured money (Divisia Index), helps in forecasting the movement of Shocks observers (Macroeconomic Indicators).

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