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The transmission of monetary policy in emerging economies during tranquil and turbulent periods
Finance Research Letters xxx (xxxx) xxxx
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The transmission of monetary policy in emerging economies during tranquil and turbulent periods☆ Jibrin Yakubua, Afees A. Salisub,c, , Abdullahi Musaa, Adebola Omosolaa, Maximillian Belonwua, Kazeem Isahd ⁎
a
Research Department, Central Bank of Nigeria, Plot 33, Abubakar Tafawa Balewa Way, Central Business District, Abuja, Nigeria Department for Management of Science and Technology Development, Ton Duc Thang University, 19 Nguyen Huu Tho Street, Tan Phong ward, District 7, Ho Chi Minh City, Vietnam c Faculty of Business Administration, Ton Duc Thang University, 19 Nguyen Huu Tho Street, Tan Phong ward, District 7, Ho Chi Minh City, Vietnam d Kogi State University, Anyigba, Kogi State, Nigeria b
ARTICLE INFO
ABSTRACT
Keywords: Monetary policy transmission BRICS SVARX Taylor rule Impulse responses Variance decompositions
We construct a theory-based interest rate channel of monetary policy transmission within an SVAR-X model for BRICS. We find a shift in the transmission of monetary policy between the tranquil and turbulent periods for BRICS particularly in Brazil, Russia and China. Thus, the transmission of monetary policy in this region can be considered episodic. We also establish the need to account for seasonal effects in the SVAR model for improved model performance.
1. Introduction The relationship between the monetary policy decision and changes in the level of output and prices is expressed by the monetary policy transmission mechanism. Accordingly, the phenomenon “monetary policy transmission (MPT)” has continued to dominate discussions in macroeconomics, particularly in the aftermath of the global financial crisis (GFC) which is widely assumed to have led to the disruption of some channels of monetary transmission. This may not be unconnected with the fact that financial crises are generally characterized with potentials that matter for the transmission of monetary policy as well as its effectiveness to stabilize output and inflation.1 However, while acknowledging all of these characteristics are capable of impairing the transmission of monetary policy (see Bouis et al., 2013; Bloom, 2014), it is equally not entirely obvious that they make monetary policy necessarily less effective. That is, they could as well amplify the effect of the transmission such that the monetary policy is able to mitigate some of the adverse characteristics of financial crises thereby bridging the adverse feedback loops between the financial sector and real economy (see for example, Bernanke et al., 1999; Mishkin, 2009). Likely to compound the above ambiguity of some of the characteristics of financial crises for the effectiveness of monetary policy is the fact that their implications for monetary policy transmission may vary for different economies. Even though, the literature is
The views in the paper are those of the authors and do not in any way reflect the official position of their institutions. Corresponding author: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam E-mail addresses: [email protected] (J. Yakubu), [email protected] (A.A. Salisu), [email protected] (A. Musa), [email protected] (A. Omosola), [email protected] (M. Belonwu), [email protected] (K. Isah). 1 See Reinhart and Rogoff (2008); Bloom (2009); D'ees and Brinca (2013) for details on the typical characteristics of financial crises. ☆ ⁎
https://doi.org/10.1016/j.frl.2019.09.010 Received 15 July 2019; Accepted 14 September 2019 1544-6123/ © 2019 Elsevier Inc. All rights reserved.
Please cite this article as: Jibrin Yakubu, et al., Finance Research Letters, https://doi.org/10.1016/j.frl.2019.09.010
Finance Research Letters xxx (xxxx) xxxx
J. Yakubu, et al.
vast on the transmission of monetary policy, the debate on whether the transmission channels of monetary policy were impaired due to the global financial crisis has received little or no attention from the perspective of emerging economies. This study therefore, empirically analyzes whether monetary policy transmission in the BRICS has different effect during the financial crisis period when compared with non-crisis period. The most important channel to determine is however, connected to interest rate, that influences the cost of bank loans and the cost of the government financial funding through the issuance of bonds. Thus, the contribution of this study is mainly twofold. We expand the conventional Taylor's Rule to include the role of seasonal effects in the hypothesis that central banks will raise interest rate when inflation is above target or when output growth is above potential. Consequently, we propose that an augmented SVAR model namely, SVAR-X is the most appropriate to reflect the probable sensitivity of monetary policy transmission in BRICS between the tranquil and turbulent periods. Following this brief background, the next section presents the SVAR model with the underlying restrictions; Section 3 discusses data issues and the results of the estimation; and Section 4 is the concluding section. 2. The Model Prior to our preference for seasonal effect–based SVAR-X as the most appropriate for modeling monetary policy transmission in BRICS, we considered different variants of SVAR ranging from the traditional VAR, VAR-X with seasonality and the variable-augmented SVAR model. The latter augments the Taylor rule interest rate equation to include nominal exchange rate. Using relevant model performance evaluation methods, such as RMSE and MAE; we find the SVAR-X which accounts for seasonal effects as the most appropriate variant of SVAR model. Thus, we construct as shown below, a three-variable SVAR-X model for the interest rate channel of monetary policy transmission:
A0 Yt =
0
+
1Yt 1
+
2 Yt 2
+ …+
p Yt p
+ Xt +
(1)
t
The channel of monetary policy transmission in Eq. (1) hinges on the traditional Taylor rule which assumes that a typical monetary policy authority responds to variations in inflation and output. Hence, Yt = [ gt t i ] is a 3 × 1 vector of endogenous variables while A0 is a 3 × 3 matrix of contemporaneous effects. The term Π0 in the specification is a 3 × 1 vector of constants; Πi, is a 3 × 3 matrix of coefficients for lagged variables, ∀ i > 0; Xt is a 3 × 1 vector of fixed regressors that account for seasonal effects; δ is a 3 × 3 diagonal matrix of coefficients for the fixed regressors; and εt is a 3 × 1 vector of error terms. The εt from the technical point of view can be described as a structural innovation or structural shock with a mean zero and also serially uncorrelated, while p which denotes the optimal lag is obtained using the Schwartz Information Criterion (SIC). It is also instructive to note that the endogenous variables gt, πt and Δi are respectively defined as industrial growth, inflation and change in nominal interest rate. Reflecting the variables in such transformation form is to circumvent the problem of unit root. In order to make the terms expressed in Eq. (1) conformable, we construct the parameters for fixed regressors in the form of a diagonal matrix as follows: 1
Xt =
0 0
0 2
0 0
0
3
X1t X2t X3t
(2)
In addition, the SVAR model requires some restrictions in order to estimate the contemporaneous effects. To do this, we are guided by the [n (n 1)/2] condition where n is the number of endogenous variables. In line with the Taylor rule, we follow the recursive approach and therefore the A0 matrix in the SVAR model becomes (see Table 1). The underlying perspective for the recursive story in Table 1 is that: (i) interest rates have no effect on the output gap in the current period; (ii) there is no direct effect of current interest rates upon inflation; and (iii) there is an interest rate rule in which the monetary authority responds to the current output growth and inflation. This nonetheless, our preference for an augmented SVAR model as earlier established is to reflect some inherent effects in the datasets. Supporting this is our finding of significant role of seasonal effect as useful insights for policy decision when evaluating monetary policy transmission in BRICS. The reduced-form of equation as required for the estimation of a standard SVAR model is represented in VAR form as follows: (3)
Yt = B (L) Yt + et
The omission of the fixed terms in Eq. (3) is mainly for notational convenience and therefore B (L) Yt = A0 1 ( 1 LYt 1 + 2 L2Yt +…+ p L pYt ) and et = A0 1 t . Since the recursive structure embodied in Table 1 is rooted on economic theory using the Taylor rule, the "orthogonalization" of the reduced-form residuals involving Cholesky decomposition can be regarded as relevant in this case. Thus, the initial impulse response of output growth and inflation to monetary policy shock can therefore be Table 1 Restrictions on the A0 matrix. Contemporaneous variable Endogenous variable gt πt Δi
gt * 1 1
πt 0 * 1
2
Δi 0 0 *
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represented in matrix form as follows:
etg et et
i
a 0 0 = b c 0 d e f
supply t demand t monetary t
(4) (etg )
The shocks in this system are given the names shock1 or supply shock; shock2 (et ) or demand shock and shock3 (et ) or monetary/ interest rate shock. The rationale behind Eq. (4) is that output does not depend contemporaneously on price and interest rate, and that could be justified by operational rigidities in the production of goods and services. However, price is expected to respond to changes in output since such rigidities as in the production case are not applicable. Nonetheless, the impact of interest rate on the general price level usually comes with some lags particularly if it is expected to impact through the production channel. It is expected that both the demand and supply shocks will give rise to unanticipated policy actions by the monetary policy authority in order to mitigate their long term consequences. i
3. Data issues and results 3.1. Data source and description The study used monthly frequency ranging from the first month of 2000 to the twelfth month of 2018. The data are mainly sourced from the International financial statistics (IFS) of the International Monetary Fund (IMF). The variables of interest are inflation measured as first difference of logged Consumer Price Index (CPI), output growth which is equally measured as first difference of logged Industrial Production Index (IPI), and interest rate measured as change in three (3) month Treasury bill rate. We also control for the role of exchange rate using first difference of logged Nominal Effective Exchange Rate (NEER). 3.2. Analyses of contemporaneous effect in the SVARX model The objective here is to test whether the central banks react contemporaneously to inflation variability and output variability. Presented in Table 2a through to Table 2c is the result for the contemporaneous effects as obtained from the estimated SVAR-X model across the full sample period (2000M1-2018M12), Pre-GFC period (2000M1-2007M09) and Post-GFC period (2007M10-2018M12). A cursory look at the tables shows evidence of contemporaneous of interest rate to shocks due to output growth and exchange rate for all the BRICS countries regardless of the data sample except for India in the post-GFC period. One striking observation is the contrasting signs on the contemporaneous effects between the pre- and post-GFC sub-samples particularly in terms of how the considered emerging countries respond to inflation variability during the two periods (see Tables 2b and 2c). For example, during the tranquil period perhaps because inflation is higher than its expected value, its relation with the policy rate is positive for Brazil and China (see Table 2b) while the reverse is the case during the crisis period where possibly inflation falls below its expected value (see Table 2c). The response only becomes evident for South Africa during the crisis period while the reverse is the case for Russia. This evidence, further attests to the shift in monetary policy transmission between the tranquil and turbulent periods. It also affirms the episodic nature of monetary policy transmission. 3.3. Analyses of impulse response and variance decomposition To determine the overall dynamic response of the interest channel of monetary policy transmission to shocks due to output growth and inflation, we estimate impulse response function within the 95% confidence interval. For each of the BRICS member Table 2A Results of contemporaneous effect for full sample. Response variable
Contemporaneous variable Brazil gt – −0.0007 (0.0066) 1.6217 (−5.74537)
gt πt Δi
Russia
πt 0 –
Δi 0 0
166.1951*** (−57.7564)
–
China gt πt Δi
– −0.0312*** (0.0116) −0.3803 (1.4397)
gt – 0.0190** (0.0080) −7.4028** (3.7042)
India πt 0 –
Δi 0 0
80.8452* (30.4072)
–
gt – 0.0977 (−0.2466) −3.4817 (−3.8046)
South Africa 0 –
0 0
16.7819*** (30.4072)
–
– 0.0310*** (0.0095) 7.4749*** (−0.5249)
3
0 –
0 0
−1.5274 (−3.5938)
–
πt 0 –
Δi 0 0
−1.1306 (−1.0302)
–
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Table 2B Pre-GFC sample. Brazil gt gt πt Δi
– 0.0152 (−0.0143) 23.8023*** (−15.0999)
πt
Δi
0 –
0 0
−376.932*** (−109.8019)
–
Russia gt – −0.0286* (0.0162) 4.8196 (7.6189)
China gt πt Δi
πt
Δi
0 –
0 0
15.2846 (48.4313)
–
India gt – −0.8317** (−0.3682) −1.7765 (−5.0436)
πt
Δi
0 –
0 0
1.4479 (−1.4133)
–
South Africa
– −0.0332*** (0.0127) −2.3201* (1.2217)
0 –
0 0
−23.0595*** (9.6841)
–
– 0.0454*** (−0.0108) 9.3759*** (−0.7162)
0 –
0 0
−12.8750** (−6.3782)
–
Table 2C Post-GFC sample. Brazil gt gt πt Δi
gt πt Δi
– −0.0065 (−0.0061) 4.5975** (−2.2719)
πt
Δi
0 –
0 0
123.756*** (−31.4207)
–
Russia gt – 0.0339*** (0.0105) −15.8192*** (4.9881)
China
South Africa
– 0.0206 (0.2335) −0.0195 (3.2539)
0 –
0 0
42.1158*** (11.8772)
–
– 0.0227* (−0.0124) 6.1489*** (−0.7368)
πt
Δi
0 –
0 0
148.561*** (39.2524)
–
India gt – 0.4998 (−0.3288) −1.0836 (−5.4655)
0 –
0 0
0.3083 (−5.0128)
–
πt
Δi
0 –
0 0
−1.9432 (−31.4207)
–
Note: The values in parenthesis are standard errors while, ***, ** and * represent 1%, 5% and 10% levels of significance.
countries, we report impulse response of the variable of interest for instance interest rate to a one standard deviation shock to output growth and inflation (see Fig. 1). Starting with the full sample period, a shock to output growth tends to prompt a short run negative response from interest rate in virtually all the BRICS countries with South Africa being the only exception. The finding is however, mixed when the sample is portioned into pre and post global financial crisis. Similarly, prior to the advent of the financial crisis, the
Fig. 1. Contemporaneous Response of Interest to Output Growth (Shock1) and Inflation Rate (Shock2). 4
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Fig. 1. (continued)
Fig. 1. (continued)
interest in each of the BRICS countries responds negatively to demand shock particularly in the first and second months, the reverse is however, the case at least for Brazil, Russia and China in the aftermath of the global financial crisis. To further complement our findings is the variance decomposition result in Table 3. The decomposition allows us to make inference over the proportion of movements in interest rate due supply shock (output growth) and demand shock (inflation). Quite interesting evidence in Table 3 is the fact that the portion of variation in interest rate that is due to supply shock and demand shock appears to be more pronounced in the pre financial crisis period when compared to the period after the crisis. The only exception in this regard is the case of Brazil. On the whole, the variance decomposition for interest rate channel of monetary policy transmission in BRICS seems comparable to some of the impulse responses presented in Fig. 1. These among others, further ascertain the sensitivity of the potential of the interest rate channel of monetary policy transmission to macroeconomic conditions. 4. Conclusion Motivated by the assertion that the effects of monetary policy during financial crises substantially differ from those in non-crisis periods, this study explores an augmented SVAR model to reflect the role of seasonal effects in the interest rate channel of monetary policy transmission in BRICS. To answer the question of whether the global financial crisis impaired the transmission of monetary 5
Brazil S.E
1 2.516 2 2.549 3 2.555 4 2.557 5 2.558 10 2.558 Pre-GFC Sample 1 3.792 2 3.849 3 3.874 4 3.884 5 3.889 10 3.895 Post-GFC Sample 1 0.847 2 0.855 3 0.856 4 0.856 5 0.856 10 0.856
Period
3.533 4.561 4.839 4.981 5.036 5.078
11.306 12.819 13.504 13.930 14.137 14.374
10.119 9.957 9.954 9.954 9.954 9.954
1.389 2.393 2.737 2.765 2.785 2.781
1.826 2.574 2.672 2.689 2.690 2.691
Shock2 (πt)
0.039 1.273 1.382 1.418 1.418 1.419
Shock1 (gt)
6 1.541 1.581 1.583 1.584 1.584 1.585
1.555 1.599 1.615 1.619 1.620 1.621
1.596 1.600 1.601 1.601 1.601 1.601
Russia S.E
Table 3 Variance Decomposition of Interest Rate in BRICS.
3.258 4.064 4.096 4.100 4.099 4.097
0.374 4.319 6.158 6.556 6.692 6.723
1.091 1.596 1.613 1.614 1.615 1.615
Shock1 (gt)
9.280 8.865 8.897 8.969 9.014 9.056
0.109 0.509 0.522 0.573 0.583 0.601
2.999 3.019 3.036 3.052 3.059 3.069
Shock2 (πt)
0.393 0.395 0.401 0.405 0.405 0.406
0.189 0.196 0.199 0.199 0.199 0.199
0.322 0.324 0.328 0.329 0.329 0.329
India S.E
0.105 0.229 0.600 0.937 1.128 1.229
0.408 2.773 2.786 3.078 3.076 3.077
0.395 1.022 1.229 1.419 1.509 1.548
Shock1 (gt)
1.370 1.828 3.616 3.830 3.832 3.831
1.161 4.576 5.393 5.417 5.469 5.476
0.533 0.984 2.576 2.598 2.599 2.602
Shock2 (πt)
0.545 0.553 0.553 0.553 0.553 0.553
0.376 0.393 0.394 0.394 0.394 0.394
0.497 0.506 0.506 0.506 0.506 0.506
China S.E
0.046 0.533 0.534 0.534 0.534 0.534
1.766 1.787 1.783 1.782 1.785 1.786
0.177 0.171 0.179 0.182 0.183 0.183
Shock1 (gt)
8.516 8.631 8.629 8.629 8.629 8.629
5.762 8.914 8.875 8.914 8.915 8.915
1.856 2.126 2.128 2.128 2.128 2.128
Shock2 (πt)
0.198 0.221 0.224 0.227 0.233 0.239
0.285 0.310 0.332 0.342 0.351 0.366
0.232 0.258 0.269 0.272 0.276 0.284
34.625 46.255 47.290 47.043 47.568 49.168
65.938 71.039 71.743 70.406 71.032 71.621
48.332 57.679 59.535 59.932 60.274 61.459
South Africa S.E Shock1 (gt)
0.002 0.275 0.587 1.174 1.377 1.553
1.491 1.283 2.999 5.234 5.686 6.667
0.042 0.078 1.142 1.567 1.587 1.781
Shock2 (πt)
J. Yakubu, et al.
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policy, we partitioned the sample period into two sub-samples to include the period before and after the financial crisis. For a reasonable number of the BRICS countries under consideration, we find that the contemporaneous response of interest rates in these economies to inflation variability and output variability in the period before the GFC differs substantially from those in the post-GFC crisis. Hence, ignoring this shift in the interest rate channel of monetary policy transmission may undermine the outcomes of monetary policy analyses and by extension lead to wrong conclusions. In addition, an SVAR model that allows for some seasonal effects which cannot be captured by a two-period partition as done in this study will improve the performance of the model. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.frl.2019.09.010. APPENDIX 1. Forecast Evaluation of Multivariate Models for MPTM BRICS BRAZIL
INDIA
RUSSIA
CHINA
SOUTH AFRICA
Traditional VAR In-sample forecast Out-ofsample forecast In-sample forecast Out-ofsample forecast In-sample forecast Out-ofsample forecast In-sample forecast Out-ofsample forecast In-sample forecast Out-ofsample forecast
VAR X- GFC Dummy
VAR X- Seasonal Dummy
RMSE MAE RMSE MAE
CPI 0.2170 0.1581 0.4326 0.3316
IPI 5.5912 4.3652 5.4471 4.3433
INTR 2.7419 0.9708 1.1253 0.7421
CPI 0.2173 0.1589 0.4292 0.3284
IPI 5.5778 4.3552 5.4413 4.3528
INTR 2.7401 0.9669 1.1322 0.7518
CPI 0.2071 0.1477 0.4471 0.3346
IPI 2.5296 2.0142 2.2369 1.6650
INTR 2.6696 1.0654 1.1888 0.8632
Variable NEER CPI 0.2170 0.1581 0.4326 0.3316
Augmented VAR IPI 5.5912 4.3652 5.4471 4.3433
INTR 2.6998 0.9859 1.0935 0.7360
RMSE MAE RMSE MAE
0.6459 0.4559 0.9916 0.7662
3.3837 2.5618 4.4198 3.6038
0.3500 0.1299 0.0724 0.0482
4.4198 3.6038 1.0282 0.8044
3.3821 2.5590 4.4167 3.6064
0.3490 0.1311 0.0764 0.0559
0.5309 0.3580 0.7062 0.5376
1.6280 1.1557 3.0724 2.1424
0.3425 0.1417 0.0850 0.0591
0.6459 0.4559 0.9916 0.7662
3.3837 2.5618 4.4198 3.6038
0.3483 0.1367 0.0729 0.0494
RMSE MAE RMSE MAE
1.6817 1.1051 1.1954 0.6890
0.3740 0.2561 0.5289 0.3730
5.6845 3.8927 10.0395 6.4515
1.6816 1.1050 5.5011 4.9007
0.3740 0.2534 10.478 7.5298
5.6847 3.8969 11.8438 10.3032
1.6139 1.0574 1.2389 0.7423
0.2885 0.1943 0.5166 0.3623
2.2802 1.6020 3.7087 2.5056
1.6450 1.0855 1.1751 0.7377
0.3650 0.2653 0.5484 0.4505
5.7912 4.0949 10.1457 7.0850
RMSE MAE RMSE MAE
0.5649 0.4407 0.6114 0.4350
0.5439 0.3192 0.3173 0.1030
2.8261 1.7070 0.5518 0.4109
0.5641 0.4393 0.6128 0.4407
0.5439 0.3189 0.3174 0.1029
2.8224 1.6978 0.5589 0.4167
0.4199 0.3243 0.3543 0.2546
0.5431 0.3822 0.3404 0.2359
3.3245 1.6522 0.6506 0.4843
0.6042 0.4695 0.6126 0.4345
0.5456 0.3342 0.3130 0.1457
3.2900 1.6629 0.5987 0.4263
RMSE MAE RMSE MAE
0.3365 0.2653 0.4810 0.3961
0.2942 0.1750 0.1009 0.0681
0.2498 0.1548 0.1087 0.0836
0.3350 0.2650 0.4843 0.4025
0.2942 0.1754 0.1013 0.0677
0.2497 0.1547 0.1094 0.0835
0.2619 0.2078 0.4200 0.3565
0.2893 0.1785 0.1030 0.0745
0.2433 0.1575 0.0987 0.0762
0.3365 0.2653 0.4810 0.3961
0.2942 0.1750 0.1009 0.0681
0.2503 0.1548 0.1126 0.0879
Note: The lowest the RMSE or MAE value the better the fit or forecast performance a model. More so, In-sample forecast sample: 2000M1-2014M12 and out-of-sample forecast sample: 2015m1 −2018m12.
References Bernanke, B.S., Gertler, M.,., Gilchrist, S., 1999. The financial accelerator in a quantitative business cycle framework. In: Taylor, J.B, Woodford, M (Eds.), Handbook of Macroeconomics 1. pp. 1341–1393. Bloom, N., 2009. The impact of uncertainty shocks. Econometrica 77 (3), 623–685. Bloom, N., 2014. Fluctuations in uncertainty. J. Econ. Perspect. 28 (2), 153–176. Bouis, R., Rawdanowicz, L., Renne, J.P., Watanabe, S.,. and Christensen, A.K. (2013). The effectiveness of monetary policy since the onset of the financial crisis. OECD economics department working paper no. 1081. D´ees, S.,., Brinca, P.S., 2013. Consumer confidence as a predictor of consumption spending: evidence for the united states and the euro area. Int. Econ. 134, 1–14. Mishkin, F.S., 2009. Is monetary policy effective during financial crises? Am. Econ. Rev. 99 (2), 573–577. Reinhart, C.M.,., Rogoff, K.S., 2008. Is the 2007 us sub-prime financial crisis so different? an international historical comparison. Am. Econ. Rev. 98 (2), 339–344.
7
Contents lists available at ScienceDirect
Finance Research Letters journal homepage: www.elsevier.com/locate/frl
The transmission of monetary policy in emerging economies during tranquil and turbulent periods☆ Jibrin Yakubua, Afees A. Salisub,c, , Abdullahi Musaa, Adebola Omosolaa, Maximillian Belonwua, Kazeem Isahd ⁎
a
Research Department, Central Bank of Nigeria, Plot 33, Abubakar Tafawa Balewa Way, Central Business District, Abuja, Nigeria Department for Management of Science and Technology Development, Ton Duc Thang University, 19 Nguyen Huu Tho Street, Tan Phong ward, District 7, Ho Chi Minh City, Vietnam c Faculty of Business Administration, Ton Duc Thang University, 19 Nguyen Huu Tho Street, Tan Phong ward, District 7, Ho Chi Minh City, Vietnam d Kogi State University, Anyigba, Kogi State, Nigeria b
ARTICLE INFO
ABSTRACT
Keywords: Monetary policy transmission BRICS SVARX Taylor rule Impulse responses Variance decompositions
We construct a theory-based interest rate channel of monetary policy transmission within an SVAR-X model for BRICS. We find a shift in the transmission of monetary policy between the tranquil and turbulent periods for BRICS particularly in Brazil, Russia and China. Thus, the transmission of monetary policy in this region can be considered episodic. We also establish the need to account for seasonal effects in the SVAR model for improved model performance.
1. Introduction The relationship between the monetary policy decision and changes in the level of output and prices is expressed by the monetary policy transmission mechanism. Accordingly, the phenomenon “monetary policy transmission (MPT)” has continued to dominate discussions in macroeconomics, particularly in the aftermath of the global financial crisis (GFC) which is widely assumed to have led to the disruption of some channels of monetary transmission. This may not be unconnected with the fact that financial crises are generally characterized with potentials that matter for the transmission of monetary policy as well as its effectiveness to stabilize output and inflation.1 However, while acknowledging all of these characteristics are capable of impairing the transmission of monetary policy (see Bouis et al., 2013; Bloom, 2014), it is equally not entirely obvious that they make monetary policy necessarily less effective. That is, they could as well amplify the effect of the transmission such that the monetary policy is able to mitigate some of the adverse characteristics of financial crises thereby bridging the adverse feedback loops between the financial sector and real economy (see for example, Bernanke et al., 1999; Mishkin, 2009). Likely to compound the above ambiguity of some of the characteristics of financial crises for the effectiveness of monetary policy is the fact that their implications for monetary policy transmission may vary for different economies. Even though, the literature is
The views in the paper are those of the authors and do not in any way reflect the official position of their institutions. Corresponding author: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam E-mail addresses: [email protected] (J. Yakubu), [email protected] (A.A. Salisu), [email protected] (A. Musa), [email protected] (A. Omosola), [email protected] (M. Belonwu), [email protected] (K. Isah). 1 See Reinhart and Rogoff (2008); Bloom (2009); D'ees and Brinca (2013) for details on the typical characteristics of financial crises. ☆ ⁎
https://doi.org/10.1016/j.frl.2019.09.010 Received 15 July 2019; Accepted 14 September 2019 1544-6123/ © 2019 Elsevier Inc. All rights reserved.
Please cite this article as: Jibrin Yakubu, et al., Finance Research Letters, https://doi.org/10.1016/j.frl.2019.09.010
Finance Research Letters xxx (xxxx) xxxx
J. Yakubu, et al.
vast on the transmission of monetary policy, the debate on whether the transmission channels of monetary policy were impaired due to the global financial crisis has received little or no attention from the perspective of emerging economies. This study therefore, empirically analyzes whether monetary policy transmission in the BRICS has different effect during the financial crisis period when compared with non-crisis period. The most important channel to determine is however, connected to interest rate, that influences the cost of bank loans and the cost of the government financial funding through the issuance of bonds. Thus, the contribution of this study is mainly twofold. We expand the conventional Taylor's Rule to include the role of seasonal effects in the hypothesis that central banks will raise interest rate when inflation is above target or when output growth is above potential. Consequently, we propose that an augmented SVAR model namely, SVAR-X is the most appropriate to reflect the probable sensitivity of monetary policy transmission in BRICS between the tranquil and turbulent periods. Following this brief background, the next section presents the SVAR model with the underlying restrictions; Section 3 discusses data issues and the results of the estimation; and Section 4 is the concluding section. 2. The Model Prior to our preference for seasonal effect–based SVAR-X as the most appropriate for modeling monetary policy transmission in BRICS, we considered different variants of SVAR ranging from the traditional VAR, VAR-X with seasonality and the variable-augmented SVAR model. The latter augments the Taylor rule interest rate equation to include nominal exchange rate. Using relevant model performance evaluation methods, such as RMSE and MAE; we find the SVAR-X which accounts for seasonal effects as the most appropriate variant of SVAR model. Thus, we construct as shown below, a three-variable SVAR-X model for the interest rate channel of monetary policy transmission:
A0 Yt =
0
+
1Yt 1
+
2 Yt 2
+ …+
p Yt p
+ Xt +
(1)
t
The channel of monetary policy transmission in Eq. (1) hinges on the traditional Taylor rule which assumes that a typical monetary policy authority responds to variations in inflation and output. Hence, Yt = [ gt t i ] is a 3 × 1 vector of endogenous variables while A0 is a 3 × 3 matrix of contemporaneous effects. The term Π0 in the specification is a 3 × 1 vector of constants; Πi, is a 3 × 3 matrix of coefficients for lagged variables, ∀ i > 0; Xt is a 3 × 1 vector of fixed regressors that account for seasonal effects; δ is a 3 × 3 diagonal matrix of coefficients for the fixed regressors; and εt is a 3 × 1 vector of error terms. The εt from the technical point of view can be described as a structural innovation or structural shock with a mean zero and also serially uncorrelated, while p which denotes the optimal lag is obtained using the Schwartz Information Criterion (SIC). It is also instructive to note that the endogenous variables gt, πt and Δi are respectively defined as industrial growth, inflation and change in nominal interest rate. Reflecting the variables in such transformation form is to circumvent the problem of unit root. In order to make the terms expressed in Eq. (1) conformable, we construct the parameters for fixed regressors in the form of a diagonal matrix as follows: 1
Xt =
0 0
0 2
0 0
0
3
X1t X2t X3t
(2)
In addition, the SVAR model requires some restrictions in order to estimate the contemporaneous effects. To do this, we are guided by the [n (n 1)/2] condition where n is the number of endogenous variables. In line with the Taylor rule, we follow the recursive approach and therefore the A0 matrix in the SVAR model becomes (see Table 1). The underlying perspective for the recursive story in Table 1 is that: (i) interest rates have no effect on the output gap in the current period; (ii) there is no direct effect of current interest rates upon inflation; and (iii) there is an interest rate rule in which the monetary authority responds to the current output growth and inflation. This nonetheless, our preference for an augmented SVAR model as earlier established is to reflect some inherent effects in the datasets. Supporting this is our finding of significant role of seasonal effect as useful insights for policy decision when evaluating monetary policy transmission in BRICS. The reduced-form of equation as required for the estimation of a standard SVAR model is represented in VAR form as follows: (3)
Yt = B (L) Yt + et
The omission of the fixed terms in Eq. (3) is mainly for notational convenience and therefore B (L) Yt = A0 1 ( 1 LYt 1 + 2 L2Yt +…+ p L pYt ) and et = A0 1 t . Since the recursive structure embodied in Table 1 is rooted on economic theory using the Taylor rule, the "orthogonalization" of the reduced-form residuals involving Cholesky decomposition can be regarded as relevant in this case. Thus, the initial impulse response of output growth and inflation to monetary policy shock can therefore be Table 1 Restrictions on the A0 matrix. Contemporaneous variable Endogenous variable gt πt Δi
gt * 1 1
πt 0 * 1
2
Δi 0 0 *
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represented in matrix form as follows:
etg et et
i
a 0 0 = b c 0 d e f
supply t demand t monetary t
(4) (etg )
The shocks in this system are given the names shock1 or supply shock; shock2 (et ) or demand shock and shock3 (et ) or monetary/ interest rate shock. The rationale behind Eq. (4) is that output does not depend contemporaneously on price and interest rate, and that could be justified by operational rigidities in the production of goods and services. However, price is expected to respond to changes in output since such rigidities as in the production case are not applicable. Nonetheless, the impact of interest rate on the general price level usually comes with some lags particularly if it is expected to impact through the production channel. It is expected that both the demand and supply shocks will give rise to unanticipated policy actions by the monetary policy authority in order to mitigate their long term consequences. i
3. Data issues and results 3.1. Data source and description The study used monthly frequency ranging from the first month of 2000 to the twelfth month of 2018. The data are mainly sourced from the International financial statistics (IFS) of the International Monetary Fund (IMF). The variables of interest are inflation measured as first difference of logged Consumer Price Index (CPI), output growth which is equally measured as first difference of logged Industrial Production Index (IPI), and interest rate measured as change in three (3) month Treasury bill rate. We also control for the role of exchange rate using first difference of logged Nominal Effective Exchange Rate (NEER). 3.2. Analyses of contemporaneous effect in the SVARX model The objective here is to test whether the central banks react contemporaneously to inflation variability and output variability. Presented in Table 2a through to Table 2c is the result for the contemporaneous effects as obtained from the estimated SVAR-X model across the full sample period (2000M1-2018M12), Pre-GFC period (2000M1-2007M09) and Post-GFC period (2007M10-2018M12). A cursory look at the tables shows evidence of contemporaneous of interest rate to shocks due to output growth and exchange rate for all the BRICS countries regardless of the data sample except for India in the post-GFC period. One striking observation is the contrasting signs on the contemporaneous effects between the pre- and post-GFC sub-samples particularly in terms of how the considered emerging countries respond to inflation variability during the two periods (see Tables 2b and 2c). For example, during the tranquil period perhaps because inflation is higher than its expected value, its relation with the policy rate is positive for Brazil and China (see Table 2b) while the reverse is the case during the crisis period where possibly inflation falls below its expected value (see Table 2c). The response only becomes evident for South Africa during the crisis period while the reverse is the case for Russia. This evidence, further attests to the shift in monetary policy transmission between the tranquil and turbulent periods. It also affirms the episodic nature of monetary policy transmission. 3.3. Analyses of impulse response and variance decomposition To determine the overall dynamic response of the interest channel of monetary policy transmission to shocks due to output growth and inflation, we estimate impulse response function within the 95% confidence interval. For each of the BRICS member Table 2A Results of contemporaneous effect for full sample. Response variable
Contemporaneous variable Brazil gt – −0.0007 (0.0066) 1.6217 (−5.74537)
gt πt Δi
Russia
πt 0 –
Δi 0 0
166.1951*** (−57.7564)
–
China gt πt Δi
– −0.0312*** (0.0116) −0.3803 (1.4397)
gt – 0.0190** (0.0080) −7.4028** (3.7042)
India πt 0 –
Δi 0 0
80.8452* (30.4072)
–
gt – 0.0977 (−0.2466) −3.4817 (−3.8046)
South Africa 0 –
0 0
16.7819*** (30.4072)
–
– 0.0310*** (0.0095) 7.4749*** (−0.5249)
3
0 –
0 0
−1.5274 (−3.5938)
–
πt 0 –
Δi 0 0
−1.1306 (−1.0302)
–
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Table 2B Pre-GFC sample. Brazil gt gt πt Δi
– 0.0152 (−0.0143) 23.8023*** (−15.0999)
πt
Δi
0 –
0 0
−376.932*** (−109.8019)
–
Russia gt – −0.0286* (0.0162) 4.8196 (7.6189)
China gt πt Δi
πt
Δi
0 –
0 0
15.2846 (48.4313)
–
India gt – −0.8317** (−0.3682) −1.7765 (−5.0436)
πt
Δi
0 –
0 0
1.4479 (−1.4133)
–
South Africa
– −0.0332*** (0.0127) −2.3201* (1.2217)
0 –
0 0
−23.0595*** (9.6841)
–
– 0.0454*** (−0.0108) 9.3759*** (−0.7162)
0 –
0 0
−12.8750** (−6.3782)
–
Table 2C Post-GFC sample. Brazil gt gt πt Δi
gt πt Δi
– −0.0065 (−0.0061) 4.5975** (−2.2719)
πt
Δi
0 –
0 0
123.756*** (−31.4207)
–
Russia gt – 0.0339*** (0.0105) −15.8192*** (4.9881)
China
South Africa
– 0.0206 (0.2335) −0.0195 (3.2539)
0 –
0 0
42.1158*** (11.8772)
–
– 0.0227* (−0.0124) 6.1489*** (−0.7368)
πt
Δi
0 –
0 0
148.561*** (39.2524)
–
India gt – 0.4998 (−0.3288) −1.0836 (−5.4655)
0 –
0 0
0.3083 (−5.0128)
–
πt
Δi
0 –
0 0
−1.9432 (−31.4207)
–
Note: The values in parenthesis are standard errors while, ***, ** and * represent 1%, 5% and 10% levels of significance.
countries, we report impulse response of the variable of interest for instance interest rate to a one standard deviation shock to output growth and inflation (see Fig. 1). Starting with the full sample period, a shock to output growth tends to prompt a short run negative response from interest rate in virtually all the BRICS countries with South Africa being the only exception. The finding is however, mixed when the sample is portioned into pre and post global financial crisis. Similarly, prior to the advent of the financial crisis, the
Fig. 1. Contemporaneous Response of Interest to Output Growth (Shock1) and Inflation Rate (Shock2). 4
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Fig. 1. (continued)
Fig. 1. (continued)
interest in each of the BRICS countries responds negatively to demand shock particularly in the first and second months, the reverse is however, the case at least for Brazil, Russia and China in the aftermath of the global financial crisis. To further complement our findings is the variance decomposition result in Table 3. The decomposition allows us to make inference over the proportion of movements in interest rate due supply shock (output growth) and demand shock (inflation). Quite interesting evidence in Table 3 is the fact that the portion of variation in interest rate that is due to supply shock and demand shock appears to be more pronounced in the pre financial crisis period when compared to the period after the crisis. The only exception in this regard is the case of Brazil. On the whole, the variance decomposition for interest rate channel of monetary policy transmission in BRICS seems comparable to some of the impulse responses presented in Fig. 1. These among others, further ascertain the sensitivity of the potential of the interest rate channel of monetary policy transmission to macroeconomic conditions. 4. Conclusion Motivated by the assertion that the effects of monetary policy during financial crises substantially differ from those in non-crisis periods, this study explores an augmented SVAR model to reflect the role of seasonal effects in the interest rate channel of monetary policy transmission in BRICS. To answer the question of whether the global financial crisis impaired the transmission of monetary 5
Brazil S.E
1 2.516 2 2.549 3 2.555 4 2.557 5 2.558 10 2.558 Pre-GFC Sample 1 3.792 2 3.849 3 3.874 4 3.884 5 3.889 10 3.895 Post-GFC Sample 1 0.847 2 0.855 3 0.856 4 0.856 5 0.856 10 0.856
Period
3.533 4.561 4.839 4.981 5.036 5.078
11.306 12.819 13.504 13.930 14.137 14.374
10.119 9.957 9.954 9.954 9.954 9.954
1.389 2.393 2.737 2.765 2.785 2.781
1.826 2.574 2.672 2.689 2.690 2.691
Shock2 (πt)
0.039 1.273 1.382 1.418 1.418 1.419
Shock1 (gt)
6 1.541 1.581 1.583 1.584 1.584 1.585
1.555 1.599 1.615 1.619 1.620 1.621
1.596 1.600 1.601 1.601 1.601 1.601
Russia S.E
Table 3 Variance Decomposition of Interest Rate in BRICS.
3.258 4.064 4.096 4.100 4.099 4.097
0.374 4.319 6.158 6.556 6.692 6.723
1.091 1.596 1.613 1.614 1.615 1.615
Shock1 (gt)
9.280 8.865 8.897 8.969 9.014 9.056
0.109 0.509 0.522 0.573 0.583 0.601
2.999 3.019 3.036 3.052 3.059 3.069
Shock2 (πt)
0.393 0.395 0.401 0.405 0.405 0.406
0.189 0.196 0.199 0.199 0.199 0.199
0.322 0.324 0.328 0.329 0.329 0.329
India S.E
0.105 0.229 0.600 0.937 1.128 1.229
0.408 2.773 2.786 3.078 3.076 3.077
0.395 1.022 1.229 1.419 1.509 1.548
Shock1 (gt)
1.370 1.828 3.616 3.830 3.832 3.831
1.161 4.576 5.393 5.417 5.469 5.476
0.533 0.984 2.576 2.598 2.599 2.602
Shock2 (πt)
0.545 0.553 0.553 0.553 0.553 0.553
0.376 0.393 0.394 0.394 0.394 0.394
0.497 0.506 0.506 0.506 0.506 0.506
China S.E
0.046 0.533 0.534 0.534 0.534 0.534
1.766 1.787 1.783 1.782 1.785 1.786
0.177 0.171 0.179 0.182 0.183 0.183
Shock1 (gt)
8.516 8.631 8.629 8.629 8.629 8.629
5.762 8.914 8.875 8.914 8.915 8.915
1.856 2.126 2.128 2.128 2.128 2.128
Shock2 (πt)
0.198 0.221 0.224 0.227 0.233 0.239
0.285 0.310 0.332 0.342 0.351 0.366
0.232 0.258 0.269 0.272 0.276 0.284
34.625 46.255 47.290 47.043 47.568 49.168
65.938 71.039 71.743 70.406 71.032 71.621
48.332 57.679 59.535 59.932 60.274 61.459
South Africa S.E Shock1 (gt)
0.002 0.275 0.587 1.174 1.377 1.553
1.491 1.283 2.999 5.234 5.686 6.667
0.042 0.078 1.142 1.567 1.587 1.781
Shock2 (πt)
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policy, we partitioned the sample period into two sub-samples to include the period before and after the financial crisis. For a reasonable number of the BRICS countries under consideration, we find that the contemporaneous response of interest rates in these economies to inflation variability and output variability in the period before the GFC differs substantially from those in the post-GFC crisis. Hence, ignoring this shift in the interest rate channel of monetary policy transmission may undermine the outcomes of monetary policy analyses and by extension lead to wrong conclusions. In addition, an SVAR model that allows for some seasonal effects which cannot be captured by a two-period partition as done in this study will improve the performance of the model. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.frl.2019.09.010. APPENDIX 1. Forecast Evaluation of Multivariate Models for MPTM BRICS BRAZIL
INDIA
RUSSIA
CHINA
SOUTH AFRICA
Traditional VAR In-sample forecast Out-ofsample forecast In-sample forecast Out-ofsample forecast In-sample forecast Out-ofsample forecast In-sample forecast Out-ofsample forecast In-sample forecast Out-ofsample forecast
VAR X- GFC Dummy
VAR X- Seasonal Dummy
RMSE MAE RMSE MAE
CPI 0.2170 0.1581 0.4326 0.3316
IPI 5.5912 4.3652 5.4471 4.3433
INTR 2.7419 0.9708 1.1253 0.7421
CPI 0.2173 0.1589 0.4292 0.3284
IPI 5.5778 4.3552 5.4413 4.3528
INTR 2.7401 0.9669 1.1322 0.7518
CPI 0.2071 0.1477 0.4471 0.3346
IPI 2.5296 2.0142 2.2369 1.6650
INTR 2.6696 1.0654 1.1888 0.8632
Variable NEER CPI 0.2170 0.1581 0.4326 0.3316
Augmented VAR IPI 5.5912 4.3652 5.4471 4.3433
INTR 2.6998 0.9859 1.0935 0.7360
RMSE MAE RMSE MAE
0.6459 0.4559 0.9916 0.7662
3.3837 2.5618 4.4198 3.6038
0.3500 0.1299 0.0724 0.0482
4.4198 3.6038 1.0282 0.8044
3.3821 2.5590 4.4167 3.6064
0.3490 0.1311 0.0764 0.0559
0.5309 0.3580 0.7062 0.5376
1.6280 1.1557 3.0724 2.1424
0.3425 0.1417 0.0850 0.0591
0.6459 0.4559 0.9916 0.7662
3.3837 2.5618 4.4198 3.6038
0.3483 0.1367 0.0729 0.0494
RMSE MAE RMSE MAE
1.6817 1.1051 1.1954 0.6890
0.3740 0.2561 0.5289 0.3730
5.6845 3.8927 10.0395 6.4515
1.6816 1.1050 5.5011 4.9007
0.3740 0.2534 10.478 7.5298
5.6847 3.8969 11.8438 10.3032
1.6139 1.0574 1.2389 0.7423
0.2885 0.1943 0.5166 0.3623
2.2802 1.6020 3.7087 2.5056
1.6450 1.0855 1.1751 0.7377
0.3650 0.2653 0.5484 0.4505
5.7912 4.0949 10.1457 7.0850
RMSE MAE RMSE MAE
0.5649 0.4407 0.6114 0.4350
0.5439 0.3192 0.3173 0.1030
2.8261 1.7070 0.5518 0.4109
0.5641 0.4393 0.6128 0.4407
0.5439 0.3189 0.3174 0.1029
2.8224 1.6978 0.5589 0.4167
0.4199 0.3243 0.3543 0.2546
0.5431 0.3822 0.3404 0.2359
3.3245 1.6522 0.6506 0.4843
0.6042 0.4695 0.6126 0.4345
0.5456 0.3342 0.3130 0.1457
3.2900 1.6629 0.5987 0.4263
RMSE MAE RMSE MAE
0.3365 0.2653 0.4810 0.3961
0.2942 0.1750 0.1009 0.0681
0.2498 0.1548 0.1087 0.0836
0.3350 0.2650 0.4843 0.4025
0.2942 0.1754 0.1013 0.0677
0.2497 0.1547 0.1094 0.0835
0.2619 0.2078 0.4200 0.3565
0.2893 0.1785 0.1030 0.0745
0.2433 0.1575 0.0987 0.0762
0.3365 0.2653 0.4810 0.3961
0.2942 0.1750 0.1009 0.0681
0.2503 0.1548 0.1126 0.0879
Note: The lowest the RMSE or MAE value the better the fit or forecast performance a model. More so, In-sample forecast sample: 2000M1-2014M12 and out-of-sample forecast sample: 2015m1 −2018m12.
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