93 © 2019 by the authors; licensee Asian Online Journal Publishing Group Asian Journal of Economics and Empirical Research Vol. 6, No. 2, 93-100, 2019 ISSN(E) 2409-2622/ ISSN(P) 2518-010X DOI: 10.20448/journal.501.2019.62.93.100 © 2019 by the authors; licensee Asian Online Journal Publishing Group Monetary Policy Transmission Channels and Economic Growth in Nigeria Iwedi Marshal 1Department of Banking and Finance, Rivers State University, Nigeria. Abstract This study empirically analyzes monetary policy transmission channels and economic growth in Nigeria using the vector auto regression model. Time series data were used for the period of 56 years (1960 to 2016) and sourced from the Central Bank of Nigeria statistical bulletin for various issues. The analyses show a good number of findings. Firstly, the unit root test results shows that all the variables of transmission channels are non-stationary at level, but appear stationary at first difference. Hence, the series are all integrated of order I (1). This of course authorized the study to proceed with the co-integration test which revealed that there is a long run relationship between monetary policy transmission channels and economic growth in Nigeria. Following the fact that the variables under study are co-integrated, the study went further to estimate the vector autoregressive model. The baseline result of the vector autoregressive model indicates that there exist a significant positive short run relationship between the channels of monetary policy transmission and macroeconomic output in Nigeria. Therefore, we conclude that interest rate and credit channels are critical channels for transmitting monetary policy impulses into the Nigeria economy. Based on this, the study recommends among others that the Nigeria monetary authority should as a matter of policy encourage and emphasize the good management of the transmission channels and this should be vigorously pursued, as it has the ability to trigger growth of the Nigeria economy. Keywords: Monetary policy, Keynesians, Monetarist, Interest rate channel, Credit channel, Economic growth. JEL Classification: E52; E12; E59; E49; E60. Citation | Iwedi Marshal (2019). Monetary Policy Transmission Channels and Economic Growth in Nigeria. Asian Journal of Economics and Empirical Research, 6(2): 93-100. History: Received: 13 May 2019 Revised: 18 June 2019 Accepted: 29 July 2019 Published: 5 September 2019 Licensed: This work is licensed under a Creative Commons Attribution 3.0 License Publisher: Asian Online Journal Publishing Group Funding: This study received no specific financial support. Competing Interests: The author declares that there are no conflicts of interests regarding the publication of this paper. Transparency: The authors confirms that the manuscript is an honest, accurate, and transparent account of the study was reported; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. Ethical: This study follows all ethical practices during writing. Contents 1. Introduction ...................................................................................................................................................................................... 94 2. Literature Review ............................................................................................................................................................................ 94 3. Model Estimation and Data ........................................................................................................................................................... 95 4. Results and Discussions .................................................................................................................................................................. 96 5. Conclusion and Recommendations ............................................................................................................................................... 99 Reference.............................................................................................................................................................................................. 100 http://crossmark.crossref.org/dialog/?doi=10.20448/journal.501.2019.62.93.100&domain=pdf&date_stamp=2017-01-14 http://creativecommons.org/licenses/by/3.0/ http://creativecommons.org/licenses/by/3.0/ http://asianonlinejournals.com/index.php/AJEER/article/view/968 https://orcid.org/0000-0001-7529-2464 http://asianonlinejournals.com/index.php/AJEER/article/view/968 https://orcid.org/0000-0001-7529-2464 http://asianonlinejournals.com/index.php/AJEER/article/view/968 https://orcid.org/0000-0001-7529-2464 Asian Journal of Economics and Empirical Research, 2019, 6(2): 93-100 94 © 2019 by the authors; licensee Asian Online Journal Publishing Group Contribution of this paper to the literature This study contributes to existing literature by examining monetary policy transmission channels and economic growth in Nigeria. 1. Introduction Over the decades, the real effect of the transmission channels of monetary policy on the economy has been a contentious area of debate in the academic literature (Bernanke and Gertler, 1995). This is because the argument among scholars about the channel through which monetary policy actions can be transmitted into the economy to achieve some selected macroeconomic goals is still yet to be ascertained. Some schools of thought believe and support the interest rate channel as the channel for transmitting monetary policy action while other scholars support and believe that the credit channel is the answer. The monetary policy transmission channel is the processes by which changes in monetary policy decision affect the rate of economic activity (Taylor, 1995). However, it has been discovered that the monetary policy transmission works through various channels such as the exchange rate channel, the interest rate channel, the bank credit channel and the assets price channel to affect different markets, institutions, sectors at different speeds and intensities (Cecchetti, 1999; Mihov, 2001; Ganev et al., 2002; Kujis, 2002; Elbourne et al., 2003; Juks, 2004; Nwosa and Saibu, 2012; Ishioro, 2013; Ndekwe, 2013; Hassan, 2015). Despite the efforts and measures taken by the nation monetary authority (CBN) in recent times, the uncertain nature of the transmission mechanism and poor understanding of the system has remained a major challenge for monetary policy (Uchendu, 2009). Nigeria as an economy has adopted different monetary policy regimes with the view that the economy will response favorably, but the poor performance of the economy in recent times further suggests that the lofty objectives of monetary policy may have been negatively affected by the inadequate knowledge of the exact channel through which monetary actions transmits to the economy. In the light of the above, however, very limited empirical works of citable significance have studied transmission channels of monetary policy in Nigeria using small data information and different methodologies; however the issue of investigating the true nature of the relationship between interest rate and credit channels of monetary policy transmission in Nigeria is still empirically unstudied and published using large data information and a more sophisticated methodology. From the forgoing, one can easily and clearly identify a lots of research gaps to be bridged and hence the main thrust of this paper is to critically analyze the impact of monetary policy transmission channels on economic growth in Nigeria using large data information from 1960 -2016. 2. Literature Review The review of literature is done in two sub-sections viz: theoretical framework and empirical review. 2.1. Theoretical Framework This paper is anchored on the economic belief of both the Keynesians and the Monetarists schools of thought. 2.1.1. Keynesians Theory The Keynesians theorize the effects of monetary policy instrument on the money market, the investment goods market and the goods and services market. Keynesian transmission mechanism states that, an increase in the money supply lowers the interest rate, which causes investment to rise and the AD curve to shift rightward thereby real GDP increasing and the unemployment rate dropping. Graphically it is represented in Figure 1 as follows: Figure-1. Keynesian transmission mechanism. Source: Jhingan (2011). 2.1.2. Monetarist Theory The Monetarist Transmission Mechanism holds that an increase in Money supply: increases in aggregate demand, which causes Real GDP and Price to increase with a fall in Unemployment while a decrease in Money supply leads to a decrease in aggregate demand, Real GDP and Prices with a rise in Unemployment. Graphically it represented as in Figure 2. Asian Journal of Economics and Empirical Research, 2019, 6(2): 93-100 95 © 2019 by the authors; licensee Asian Online Journal Publishing Group Figure-2. Monetarist transmission mechanism. Source: Jhingan (2011). 2.2. Empirical Review To the best knowledge of the researcher, there were few numbers of available empirical works conducted concerning the transmission channels of monetary policy in the case of Nigeria. For example, Chuku (2009) carried out such a study in Nigeria. The author used quarterly data from 1986Q1-2008Q4 and applies SVAR model in measuring the impact of monetary policy innovations in the country. His paper reveal that money supply (M2) as a quantity anchor has a moderate effect on both output and prices, while the monetary policy rate (MPR) and real effective exchange rate (REER) have neutral effect on output. Also Nwosa and Saibu (2012) investigated the transmission mechanism of monetary policy impact on the output of different sectors of the Nigerian economy. They however used the VAR methodology and found both the interest rate and exchange rate as the most effective channels of stimulating output growth of most of the sectors in the country. Chimobi and Uche (2010) employed the co-integration technique and causality test to examine the relationship between money and real economic variables in Nigeria. Their study reveals that no long run relationship between money and real economic variables. However, money supply was found to have a causal effect on both output and prices. In contrast, Harcourt et al. (2011) adopting the techniques of Vector Error Correction Model (VECM) and co integration test, found that there is long run relationship among money supply, minimum rediscount rate and treasury bill rate in Nigeria. The study also reveals that while minimum rediscount rate impacts on inflation at lag 2, money supply does not. Fasanya et al. (2013) study show that inflation, exchange rate and external reserves constitutes the most effective tools of monetary policy that promote growth of the Nigerian economy. Ishioro (2013) study examines the channels of monetary transmission mechanism in Nigeria. The study employed the granger causality test and the test shows that three channels of interest rate, exchange rate and the credit channels are functional in Nigeria. Ndekwe (2013) found that the credit channel in the financial market for the supply of credit to private sector gives the greatest effect in the way monetary policy is transmitted to the economy. He also realized that interest rate and exchange rate channels at the period 1981-2008 have weak effect on the real economy. Also, more recently, Ismail (2014) using the same techniques of co integration and VECM during the period of 1975-2010, also found co integrating relationship exists between the monetary policy variables and the real economic variable (RGDP) in Nigeria. Obafemi and Ifere (2015) investigated the mixed evidence on the effectiveness of monetary policy transmission by exploring the quarterly data of the period 1970 to 2013, tested using the FAVAR model with 53 variables. The results supported that interest rates and credit channels are the dominant and strongest channel of transmission of monetary shocks in Nigeria. While the exchange rate, and stock channel shows weak impact in the transmission process. The study by Omolade and Ngalawa (2017) in Nigeria employed structural variance decomposition approach (SVAR) to examine monetary policy transmission mechanism and manufacturing output growth in Libya and Nigeria. The authors document that exchange rate regime has some influences on the monetary policy transmission and its effectiveness on the manufacturing output growth in the two oil exporting countries. 3. Model Estimation and Data The estimations are carried out on yearly data spanning 1960 to 2016. For Nigeria, the data series cover interest rates channel (monetary policy rate, prime and maximum lending rates and deposit rate, and 90-day T-bills rate), for credit channel it includes credit to the core private sector, credit to the government, credit to SMEs and net domestic credit to the economy, while real GDP was used as the dependent variable in the model specifications. 3.1. Model Specification Following the previous works of Chileshe et al. (2014) we model the monetary policy transmission channels in Nigeria as follow. The first model below is used to capture the interest rate channel while the next captures the credit channel. = ( ) (1) Equation 2 presents the estimable version of Equation 1. + (2) = + 0 0 ;,1    i n i itE (3) = ( ) (4) Equation 5 presents the estimable version of Equation 4. Asian Journal of Economics and Empirical Research, 2019, 6(2): 93-100 96 © 2019 by the authors; licensee Asian Online Journal Publishing Group We can rewrite the model of credit channel to have the estimable version in Equation 5. + (5) = + 0 0 ;,1    i n i itE (6) Where RGDP = Real Gross Domestic Product Growth Rate. MPR = Monetary Policy Rate. MLR = Maximum Lending Rate of Deposit Money Banks. PLR = Prime Lending Rates of Deposit Money Banks. TBR = Treasury Bills Rates. DPR = Deposit Rate of Deposit Money Banks. CPS = Credit Private Sector. CGO = Credit to the Government. NDC = Net Domestic Credit to the Economy. CSM = Credit to Small and Medium Enterprises. = Constant / Intercept. - = Coefficients of independent variables.  it = Error Term. 4. Results and Discussions Table-1.Unit root test for interest rate channel variable. Items D(RGDP) D(MPR) D(TBR) D(MLR) D(PLR) D(DPR) ADF statistics -7.727133 -9.717338 -8.278697 -9.721453 -11.72617 -8.611123 1% -3.555023 -3.555023 -3.555023 -3.555023 -3.555023 -3.555023 5% -2.915522 -2.915522 -2.915522 -2.915522 -2.915522 -2.915522 Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Source: E-view 9.0 output. Table-2.Unit root test for credit channel. Items D(RGDP) D(CPS) D(CGO) D(CSM) D(NDC) ADF statistics -7.727133 -6.479150 -12.16586 -9.453190 -13.03563 1% -3.555023 -3.555023 -3.555023 -3.555023 -3.555023 5% -2.915522 -2.915522 -2.915522 -2.915522 -2.915522 Probability 0.0000 0.0000 0.0000 0.0000 0.0000 Source: E-view 9.0 output. The study conducted stationarity test using the Augmented Dickey Fuller (ADF) test. The results are summarized and presented in Table 1 and Table 2 for each of the variables studied. From the table displaying the result it is obvious that all the interest rate channel variables and credit channel variables were non stationary at levels but appears stationary at first difference. Hence, the series are all integrated of order I (1). This is evidence by the fact that the absolute values of the ADF test statistics are all greater than the MacKinnon critical values at 1% and 5% level of significance and thus the respective null hypotheses of non-stationarity are rejected, implying the absence of unit roots among the variables. Table-3.Vector autoregression results. Items RGDP MPR TBR MLR PLR DPR Lag 1 0.601772 (0.14616) [ 4.11721] 1.362664 (1.49336) [0.91248] 0.227538 (1.00227) [ 0.22702] 0.341193 (0.66646) [ 0.51195] -0.056965 (0.61548) [-0.09255] 0.842714 (0.77346) [ 1.08954] Lag 2 -0.229958 (0.14501) [-1.58577] 1.199856 (1.67295) [0.71721] 1.045003 (1.25611) [ 0.83194] 0.397281 (0.67848) [ 0.58554] -0.256329 (0.62775) [-0.40833] -0.739319 (0.74293) [-0.99513] Constant 10.93097 (3.78523) [ 2.88779] 0.753832 (1.00051) [ 0.75345] 0.684720 (1.25215) [ 0.54683] 0.947191 (1.48579) [ 0.63750] 1.787844 (1.18116) [ 1.51363] 1.125876 (1.17772) [ 0.95598] R-squared 0.678640 Adj. R-squared 0.529680 F-statistic Prob Log likelihood Akaike AIC Schwarz SC Durbin Watson 13.50635 0.000087 -187.9030 7.305563 7.780023 2.078935 Source: E-view 9.0 output. A cursory look at Table 3, it can be seen from the result that the coefficient of monetary policy rate (MPR) was positively and not significant related to Real GDP at lag 1 and 2. The MPR coefficient records a positive value of 1.362664 and 1.199856 with a t-value of 0.91248 and 0.71721 at lag 1 and 2 respectively. This implies that MPR is an important interest rate channel variable that explains future path of Nigeria economic growth. Asian Journal of Economics and Empirical Research, 2019, 6(2): 93-100 97 © 2019 by the authors; licensee Asian Online Journal Publishing Group Table-4.Variance decomposition analysis for interest rate channel. Variance decomposition of RGDP: Period S.E. RGDP MPR TBR MLR PLR DPR 1 8.434337 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 2 10.11961 96.95490 0.005617 0.071339 0.785662 0.392427 1.790055 3 10.43453 92.96489 0.923625 2.083647 1.190620 0.476967 2.360251 4 10.65481 89.17154 2.654280 3.571297 1.408247 0.913575 2.281065 5 10.79910 86.82970 4.562318 3.656825 1.429113 1.301516 2.220527 6 10.90015 85.25431 6.006015 3.590357 1.437480 1.513553 2.198282 7 10.98339 84.07429 7.190904 3.536191 1.425737 1.585936 2.186947 8 11.05811 83.03592 8.201333 3.517040 1.407892 1.680023 2.157788 9 11.12375 82.10323 8.997801 3.538194 1.392591 1.825263 2.142924 10 11.18126 81.27781 9.630680 3.572409 1.380743 1.994862 2.143495 Variance decomposition of MPR: Period S.E. RGDP MPR TBR MLR PLR DPR 1 2.229357 0.962426 99.03757 0.000000 0.000000 0.000000 0.000000 2 3.201704 0.592556 77.24688 7.065998 0.107096 11.22560 3.761874 3 3.503843 1.352799 78.62124 6.027942 1.067849 9.762551 3.167618 4 3.878872 1.634242 80.01638 5.988309 0.935444 8.826729 2.598899 5 4.173937 1.700975 78.21249 5.786780 0.841475 10.49983 2.958450 6 4.373634 1.622943 77.38652 5.692269 0.863504 11.39263 3.042140 7 4.551176 1.542700 76.66719 5.666532 0.901950 12.13302 3.088602 8 4.701110 1.499726 76.18800 5.548143 0.989095 12.65834 3.116704 9 4.827150 1.485237 75.99217 5.384562 1.090249 12.96958 3.078205 10 4.935917 1.487787 75.91877 5.221370 1.198246 13.14594 3.027884 Variance decomposition of TBR: Period S.E. RGDP MPR TBR MLR PLR DPR 1 2.790077 0.182549 74.08539 25.73206 0.000000 0.000000 0.000000 2 4.007012 0.198258 57.26735 31.32728 0.701355 4.650702 5.855063 3 4.328164 0.747484 60.77545 27.61258 1.836367 3.987794 5.040329 4 4.684304 0.926509 65.06314 24.05664 1.568509 3.822207 4.562996 5 4.963857 0.911704 65.08182 21.56190 1.623468 5.640063 5.181042 6 5.145441 0.853053 65.16276 20.11442 1.939184 6.563966 5.366618 7 5.304011 0.806471 65.33822 18.97537 2.247383 7.216091 5.416459 8 5.440150 0.782931 65.52014 18.04100 2.610468 7.636690 5.408772 9 5.555371 0.779243 65.78101 17.30545 2.970189 7.843952 5.320155 10 5.655542 0.786133 66.03663 16.71488 3.308435 7.932286 5.221640 Variance decomposition of LDR: Period S.E. RGDP MPR TBR MLR PLR DPR 1 3.310672 0.240731 68.97141 0.468549 30.31931 0.000000 0.000000 2 4.221571 1.008619 60.77094 0.785295 20.81065 16.60810 0.016409 3 4.573026 1.057218 59.82039 3.191765 18.13566 17.37432 0.420651 4 5.185386 0.874255 55.87862 9.363598 15.11311 17.49793 1.272489 5 5.657975 0.783283 52.92377 11.31902 12.90277 19.89322 2.177947 6 5.962704 0.759949 52.22219 12.02319 11.66142 21.08746 2.245790 7 6.222071 0.805485 52.30239 12.41259 10.73198 21.58651 2.161037 8 6.448688 0.906820 52.71578 12.54829 10.00220 21.76207 2.064834 9 6.647557 1.015566 53.25193 12.55158 9.420467 21.79075 1.969709 10 6.824151 1.107376 53.77078 12.52034 8.944543 21.76533 1.891622 Variance decomposition of PLR: Period S.E. RGDP MPR TBR MLR PLR DPR 1 2.631887 0.244115 27.49415 0.887946 6.481833 64.89195 0.000000 2 3.138912 1.401136 31.44379 1.795441 7.392059 53.91792 4.049655 3 3.723156 1.271320 43.35407 4.578869 5.329843 42.58312 2.882774 4 4.223003 1.455586 46.94058 5.168531 4.333745 39.01658 3.084971 5 4.564896 1.524997 50.58178 5.230643 3.826601 36.06926 2.766712 6 4.872764 1.585287 53.16908 5.239486 3.444569 34.01944 2.542142 7 5.128340 1.644133 55.05052 5.185666 3.208963 32.49095 2.419771 8 5.347064 1.677008 56.54160 5.117042 3.032927 31.31165 2.319774 9 5.535099 1.695508 57.66590 5.040768 2.914516 30.42220 2.261114 10 5.696536 1.703037 58.56735 4.962703 2.834927 29.71028 2.221704 Variance decomposition of DPR: Period S.E. RGDP MPR TBR MLR PLR DPR 1 2.624210 0.009943 48.58470 0.270150 2.005392 11.64658 37.48324 2 3.457687 0.135769 44.79117 1.646227 3.278928 23.98137 26.16654 3 3.877004 0.254214 45.59682 1.424333 6.583039 23.46897 22.67262 4 4.261912 0.210638 49.45005 1.223300 7.656603 21.38626 20.07315 5 4.603639 0.228123 51.73159 1.149316 8.690461 20.19295 18.00756 6 4.873761 0.276649 53.19055 1.385435 9.865343 18.91768 16.36434 7 5.101285 0.321693 54.50980 1.587383 10.72062 17.75040 15.11011 8 5.297389 0.352962 55.47291 1.780755 11.39475 16.80909 14.18953 9 5.463912 0.365877 56.09549 2.001014 11.98258 16.05494 13.50010 10 5.605358 0.367229 56.46151 2.232607 12.52078 15.44088 12.97699 Cholesky ordering: RGDP MPR TBR MLR PLR DPR Source: E-view 9.0 output. On the other hand, coefficient for Treasury bill rate (TBR) was positively and not significantly related to Real GDP of Nigeria at both lag 1 and 2. The TBR coefficient was positive with this value 0.227538 and 1.045003 with a t-value of 0.22702 and 0.83194 respectively at lag 1 and 2. Again the coefficient of maximum lending rate of deposit money bank (MLR) shows a positive and not significant relationship to Real GDP at lag 1 and 2 with the value of 0.341193 and 0.397281 and a t-value of 0.51195 and 0.58554 respectively. Similarly, the coefficient Asian Journal of Economics and Empirical Research, 2019, 6(2): 93-100 98 © 2019 by the authors; licensee Asian Online Journal Publishing Group estimate for prime lending rate (PLR) was negatively signed and not significantly linked to real GDP when lagged both in first and second period. The PLR coefficient value was (-0.056965) with a t-value of (-0.09255) at lag 1 and the coefficient value of -0.256329 and t-value of -0.40833 at lag 2. Finally, the parameter estimate for deposit rate (DPR) was positively signed and not significantly related to real GDP when lag at one period. The coefficient has a positive value of 0.842714 with a t-value of 1.08954 but turned negative and significant with the value of -0.739319 as coefficient and -0.995113 as the t-value at lag 2. However, a look at the VAR global statistic results shows that the observed degree of relationship between real economic output and the interest rate channels variables stood at an R squared of 0.783486. This implies that about 78 percent of the variations in growth of the Nigeria economy were explained by changes in interest rate channel variables. This shows that the direct link from interest rate channel to growth of the Nigeria economy has historically been strong. The results of the variance decomposition in Table 4 shows that short run shock to RGDP accounted for 100% variation of fluctuation in RGDP (own shock) and 0% from other variables in that period. The result also showed that the variation in RGDP as accounted for by its own shock happens to be the highest and varies from 100% in the first period to 81.27% over the 10 period horizons. Also in the short run i.e. period two, shock to RGDP account for 0.59% changes in fluctuations in monetary policy rate (MPR) and 1.49% in the long run that is the 10th period. About 99% of variation in fluctuations in MPR is as a result of own shock in the first period. Also, in period two, a shock in RGDP account for 0.20% variations in fluctuation in TBR (Treasury Bill Rate), while 10th period i.e. (long run), it account for 16.71% with 25.7% shocks as a result of own shock in the first period. RGDP shocks account for 0.24% variation of fluctuation in LDR in the short run and 1.11% in the long run with 30.3% being accounted for from own shock in the first period. Similarly about 0.24% changes in fluctuations in LDR are accounted by Real GDP shock in the first period and 1.70% at the long run while 64.9% being accounted for by its own shock. Finally, for DPR it also account for 0.01% variation in fluctuations in the short run and 0.37% on the long run while for its own shock it recorded 37.5% and 12.9% for both short and long run respectively. Table-5.Vector autoregression estimates for credit channel. Items RGDP CPS CGO CSM Lag 1 0.690180 (0.14350) [ 4.80973] 0.459879 (0.00430) [ 1.21243] 0.000762 (0.00140) [ 2.54544] -0.082126 (0.33406) [-0.24584] Lag 2 -0.209061 (0.14368) [-1.45503] -0.554997 (0.00015) [-1.75550] -0.000158 (0.00139) [-0.11419] -0.176417 (0.31351) [-0.56271] Constant 6.812128 (3.20003) [ 2.12877] 3.663073 (1.61279) [ 2.27126] 144.7818 (349.812) [ 0.41388] 2.308926 (1.48328) [ 1.55663] R-squared 0.783486 Adj. R-squared 0.667005 F-statistic Prob Log likelihood Akaike AIC Schwarz SC Durbin Watson 13.50635 0.000290 -189.6974 7.298088 7.699555 2.035670 Source: E-view 9.0 Output. The VAR results of the relative statistics are summarized on Table 5. It can be seen that the parameter estimate for growth in credit to the private sector (CPS) has a short run positive and significant relationship with Real GDP at lag 1. It turned negative at lag 2. The variable recorded a coefficient 0.459879 and -0.554997 with a probability and standard error of 0.00015 & 0.000430 at t-value of 1.75530 and 1.21243 respectively. Similarly, the coefficient estimate for growth in credit to the government (CGO) also had short run positive relationship with Real GDP. This is statistically significant at lag 1 but turned negative when it was lagged 2. CGO as a variable of the credit channel recorded a coefficient of 0.000762 and -0.000158 at both lag 1 and 2. The standard error stood at 0.00140 and 0.00139 with a t-statistics of 0.54544 and -0.24584 for both lag. Also, the parameter estimate for growth in credit to SMEs (CSM) has a negative short run relationship with Real GDP when lagged at period 1 and 2. CSM recorded a coefficient of -0.082126 and -0.176417, a standard error of 0.33406 and 0.31351 and a t-value of -0.24584 and -0.56271. However, the observed degree of relationship between the variables of credit channel and economic growth was quite high at an adjusted R squared of 0.667. By implication, about 67% of the variations in Real GDP were explained by changes in credit channel variables. This demonstrates a good fit as indicated by the F- statistic of 13.506. The log likelihood ratio, Akaike information criterion and Schwarz Bayesian criterion statistic all showed that the model has good forecasting power. Thus the credit channel of monetary policy transmission mechanism has short run relationship with Real GDP. Therefore, the null hypothesis of no significant short run relationship cannot be accepted in place of the alternative hypothesis. The result of the variance decomposition analysis for credit channel as presented in Table 6 shows that changes in the variation in real GDP accounted by its own shock seem to be the highest and changes from 100% within the first period to 85.96% over ten (10) period horizons. For growth in credit to the private sector (CPS), the result also revealed that the variations in real GDP accounted for by credit channel variables are low and started from 0.000 in the first period for CPS, CGO, CSM and NDC to 2.15%, 0.18%, 0.45% and 1.29% in the second period to about 2.33%, 0.17%, 3.29%, and 1.20% in the fourth period than 2.90%, 0.19%, 7.12% and 3.68% and 2.90%, 0.20%, 7.23% and 3.71% in the ninth and tenth period horizon respectively. As regards credit to private sector (CPS), the variance decomposition result shows that changes in CPS accounted for by its own shock is the highest and changes from 99.7% in the period to 74.52% in third period to 73.73% in the tenth period. Asian Journal of Economics and Empirical Research, 2019, 6(2): 93-100 99 © 2019 by the authors; licensee Asian Online Journal Publishing Group Table-6. Variance decomposition analysis. Variance decomposition of RGDP: Period S.E. RGDP CPS CGO CSM NDC 1 8.513706 100.0000 0.000000 0.000000 0.000000 0.000000 2 10.62898 95.91622 2.153178 0.187866 0.453195 1.289540 3 10.97646 94.79788 2.093407 0.176474 1.722649 1.209589 4 11.10055 93.00190 2.333663 0.173248 3.289624 1.201567 5 11.27434 90.16023 2.763084 0.168374 4.918100 1.990212 6 11.41178 88.00253 2.918916 0.170454 6.003359 2.904743 7 11.48723 86.85575 2.930072 0.184259 6.613413 3.416508 8 11.52235 86.33435 2.916123 0.194995 6.942033 3.612495 9 11.53917 86.08811 2.907760 0.199433 7.123093 3.681599 10 11.54812 85.95792 2.904956 0.200790 7.228336 3.707998 Variance decomposition of CPS: Period S.E. RGDP CPS CGO CSM NDC 1 4.290857 0.228812 99.77119 0.000000 0.000000 0.000000 2 5.606817 0.137677 86.00194 0.483241 0.716047 12.66109 3 6.408449 0.105563 76.36918 0.478326 1.007384 22.03955 4 6.675994 0.098216 74.52119 0.682125 0.953307 23.74516 5 6.792062 0.103760 74.02347 0.813357 0.926071 24.13334 6 6.847893 0.116362 73.89031 0.849597 0.946119 24.19761 7 6.878504 0.128779 73.82853 0.861388 0.993735 24.18757 8 6.895708 0.137957 73.78851 0.866216 1.051314 24.15601 9 6.905732 0.144124 73.75700 0.868142 1.108895 24.12184 10 6.911874 0.148307 73.73036 0.868699 1.160189 24.09244 Variance decomposition of CGO: Period S.E. RGDP CPS CGO CSM NDC 1 930.6796 0.046527 0.279977 99.67350 0.000000 0.000000 2 944.7250 0.064122 0.765088 96.73188 0.009046 2.429863 3 949.6329 0.114879 0.983879 96.02844 0.447603 2.425198 4 951.3423 0.170898 1.190525 95.68415 0.517420 2.437010 5 952.0526 0.180696 1.253276 95.54227 0.588060 2.435696 6 952.5048 0.183216 1.286119 95.45211 0.642363 2.436196 7 952.8129 0.184456 1.303185 95.39059 0.686941 2.434831 8 953.0378 0.185532 1.315494 95.34564 0.719145 2.434184 9 953.1952 0.186436 1.324494 95.31418 0.741167 2.433727 10 953.3040 0.187099 1.330946 95.29241 0.756209 2.433333 Variance decomposition of CSM: Period S.E. RGDP CPS CGO CSM NDC 1 3.946292 0.009126 4.732927 1.051240 94.20671 0.000000 2 4.837075 2.245935 9.670467 0.930410 85.65812 1.495064 3 5.541914 2.699962 10.81289 0.711073 81.40366 4.372413 4 5.906846 2.948474 12.67108 0.632347 79.79468 3.953413 5 6.137277 2.988809 13.51381 0.587202 79.13922 3.770962 6 6.290370 2.985850 14.03121 0.559595 78.77341 3.649936 7 6.393698 2.980258 14.35869 0.542005 78.53396 3.585087 8 6.464403 2.977546 14.59224 0.530361 78.35885 3.541006 9 6.512655 2.977040 14.76261 0.522621 78.22946 3.508269 10 6.545651 2.977037 14.88470 0.517451 78.13653 3.484277 Variance decomposition of NDC: Period S.E. RGDP CPS CGO CSM NDC 1 48.71597 0.077010 10.60329 2.451312 8.633990 78.23439 2 49.10961 0.754033 10.44652 2.960539 8.771397 77.06751 3 49.51701 0.852704 10.42489 3.304459 8.930783 76.48717 4 49.57541 0.860854 10.45235 3.300195 9.042889 76.34371 5 49.63941 0.858643 10.50200 3.293769 9.098248 76.24734 6 49.68093 0.857266 10.51992 3.290099 9.138086 76.19463 7 49.70097 0.856608 10.52374 3.289081 9.158034 76.17254 8 49.70973 0.856411 10.52373 3.288937 9.168294 76.16263 9 49.71324 0.856382 10.52325 3.288918 9.173466 76.15799 10 49.71480 0.856379 10.52287 3.288879 9.176207 76.15567 Cholesky ordering: RGDP CPS CGO CSM NDC Source: E-view 9.0 output. 5. Conclusion and Recommendations From the results, we conclude that the interest rate channel and the credit channel are significant channels for transmitting monetary policy actions in Nigeria. This is not a surprising outcome due to the fact that these channels significantly influences and promote growth of the economy. A good understanding of these channels through which monetary policy actions can be transmitted into the economy is very critical for a wide range of macroeconomic policy formulation and implementation. For instance in stabilizing macroeconomic output and prices, which monetary policy instrument can be put to work in the case of business cycle and high inflationary pressure, which monetary policy instruments are appropriate to tackle it. These and more other pertinent policy Asian Journal of Economics and Empirical Research, 2019, 6(2): 93-100 100 © 2019 by the authors; licensee Asian Online Journal Publishing Group question should be examined in developing economies like Nigeria. Based on this, some very pertinent recommendations were offered from the empirical findings of this study as follows: i Nigeria monetary authority should persistently adopt the use of changes in its monetary policy rate (MPR) as a policy strategy to effects changes in the credit supply and its accessibility by the productive sector of the economy. With such measures, the economy we experience changes in the credit market and the institutions. As it is through this mechanism, that the interest rate and credit channels would impact on the economy positively. ii In periods of perceived down-turn in economic activity, the CBN should employ the expansionary monetary policy tool of lowering the MPR to stimulate the credit channel, it supply and accessibility with the view of stimulating output growth, enhance employment generation and to better the general well- being of the economy without losing sight of its commitment to sustaining confidence in the monetary and financial system. iii To trigger growth through the credit channel, managers of the Nigeria economy should improve on financial regulatory reform while the country judicial system should be strengthen. 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