Monetary Policy Transmission Mechanism in Nigeria: A Comparative Analysis

Abstract

Empirical evidence has shown that the result of the exact channel of monetary policy transmission in Nigeria is mixed. This paper compared the Factor-augmented vector-auto regression (FAVAR) framework which exploits large data set of 53 with the traditional VAR model that estimates 6 variables to ascertain the exact channel of transmission. Findings from the two models conclude that although both methods generate qualitatively related results, but the FAVAR model is a superior alternative over VAR on grounds that monetary policy shocks are better identified using the FAVAR model. Also the FAVAR model does not exhibit the prize puzzle problem found in the VAR but allows for the computation of impulse responses of a large number of variables. Results from both models show that interest rate and credit channels are dominant and strongest channels of monetary policy transmission in Nigeria. Exchange rate and money channels were not significant and pronounced.

Is there any relationship between monetary policy tools and external credit-growth nexus in Nigeria?

“…This is not surprising as economic buoyancy of developed economies may normally transmit certain economic benefits, as well as economic disturbances, to their developing partners. This is related to the previous studies by Han (2012), Helbling, Huidrom, Kose and Otrok (2011) and Obafemi and Ifere (2015), although those studies are centrally on developed economies.…”

The low sensibility of monetary transmission mechanism in low-income countries

“…From this study, we took the assumption on the low responsiveness of credit channel to monetary policy shock in particular country. Next study, which was influential for us, had been provided by Obafemi and Ifer (2015) and they had constructed FAVAR (Factor-Augmented Vector Autoregression Model) for Nigeria [1], which was enlarged by financial market factor's massive. Their paper has a common feature with previous work in that the authors additionally used, to estimate monetary transmission, the disaggregated dynamic stochastic general equilibrium model (DSGE).…”

“…We did not set an objective is to investigate whether the effects of monetary policy shocks on bank lending rates systematically differ in low-income countries from what they tend to be in advanced and emerging economies, but we were very keen to explore whether these differences are consistent with conventional economics. In this scientific research, we abandoned employing the foreign monetary policy shock impulse into the recursive structure model (1).…”

“…The level of income in these countries varies from 2 310 (for Ukraine) to 12 680 US dollars (evidenced in Poland) 1 . We can admit that, during recent 4 years, the income per capita has gotten worse in Ukraine by 22,3 %, while for the other countries in the panel have experienced long series of increase in this figure: for Poland, at the rate of 4,4% annually within 10 years, which before slightly decrease in 2016, for Algeria at annual level of 3,7%, for Nigeria by 7,6% annually, but then income's value has slightly eased in 2016 on the level of 12,4%, and finally Bangladesh demonstrated growth at 9,1% of annual rate.…”

“…Due to low importance of stock exchange market in Algeria (accordingly to latest statistical publications, it has accounted for only 12 percentage points to GDP or 190 mln. US dollars in absolute terms in 2016 [16], while total deposits in banks account for more than 52%) the massive of stock prices was excluded from recursive equation (1). For Ukraine and Bangladesh transmission models, we kept the stock price indices due to a risen stake of the equity market in these countries in from 8,3-11,9% and 4,8 to 34,5% respectively 2 .…”