Effects of inflation uncertainty and exchange rate volatility on money demand in Pakistan: Bayesian econometric analysis

Abstract

The study explores determinants of money demand (M2) in a developing economy of Pakistan using annual data ranging 1976-2017. The study contributes by two ways. Firstly, it specifically examines the potential impact of exchange rate volatility and inflation uncertainty along with conventional determinants on money demand in Pakistan. Secondly, Bayesian Statistical inference is employed and the estimates are interpreted along with highest posterior density intervals (HPDI) with probability statements in order to cope with the issue of the instability of parameters. The analysis explores the positive impact of real income as well as share prices and the negative effect of interest rate on money demand as predicted by theory and earlier findings. Significant negative estimate of exchange rate shows that substitution effect overcomes wealth effect during exchange rate variations. But magnitude of exchange rate elasticity seems to depend upon strength of depreciation rate and/or future expectations, which may be considered an important research question for future work. Insignificance of exchange rate volatility for money demand in Pakistan is found which requires further research as to know about the behaviour of wealth effect and expectation effect during forex market uncertainty. Most importantly, inflation uncertainty is found to be a potential determinant of money demand in Pakistan. Rising uncertainty of inflation rate seems to have direct effects on precautionary and transaction motives of the demand for money. Findings of this study provides useful insights for policymaking in Pakistan as well as in other developing economies by suggesting inflation uncertainty as the potential determinant of money demand. Finally, Bayesian estimates along with HPDI estimates provide new information about the effects and particularly about the possible range of elasticities.

Bayesian versus Classical Econometric Inference to Revisit the Role of Human Capital in Economic Growth

“…ese priors are multiplied with the likelihood function to obtain posterior density. A detailed derivation of the likelihood function and posterior distribution may be seen in Akbar [9]. e final form of posterior density to be used for inference is derived as follows.…”

“…e Bayesian inference might be appropriate to tackle the Lucas critique by assuming parameters as random variables. However, the literature on economics has limited studies containing formal application of Bayesian econometric techniques (except Bayesian model averaging) to analyze real economic phenomena [8,9]. Selecting an important topic of economic growth, the present study is conducted to revisit the phenomenon by comparing the performance of classical and Bayesian econometric frameworks.…”

The Effect of Policy Uncertainty on Money Demand in CEMAC Countries

“…Furthermore, the literature is rich in other relevant contributions that have estimated the effect of uncertainty on the demand for money. To just mention few, we have, Hall and Noble (1987), Bahmani-Oskooee et al, (2012), Bahmani-Oskooee et al, (2013, Özdemir and Saygılı (2013), Bahmani-Oskooee and Kones (2014), , Bahmani-Oskooee et al, (2016), Bahmani-Oskooee andMaki-Nayeri (2018a,b,c;2020a, b), Gan (2019), Bahmani-Oskooee and Arize (2020), Hossain and Arwatchanakarn (2020), Mera et al, (2020), Ongan and Gocer (2021), Murad et al, (2021), andAkbar (2023).…”

A neural network model for predicting price changes of the exchange rate EUR/USD

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