Bernanke and Blinder (1988) showed in their paper that it is possible that the bond rate would increase in the event that income increases. Their model showed that the traditional IS-LM model that was already in place is inadequate in
determining the exact effects of the bond rate when bank reserves increase. Once the loan market was placed into the IS-LM model to become their CC-LM model, a monetary expansionary policy would shift both the LM and CC curves, rendering the bond rate change undetermined. As described, there are cases where the bond rate could increase, decrease, or remain constant. These cases all depend on whether the LM curve shift is greater than the CC curve, the other way around, or if both curves shifts are equal to each other. Also, after solving the equations, Bernanke and Blinder also showed that the same monetary policy and resulting possible shifts in the LM and CC curves could result in different changes in the loan rate, too.
With reserve money set as the exogenous policy variable, the model showed that a change in monetary policy will have a positive impact on the overnight rate. In previous theories, such a change would have an inverse impact on the overnight rate, but what is shown here is that Taiwan’s credit channel doesn’t necessarily follow the previous theoretical model. Also, the model showed that while there is a positive change in the loan rate, this change is insignificant. A possible explanation is that in Taiwan’s credit channel, the loan rate is unaffected by any monetary policy change.
This means that any monetary policy change will only affect total output and the loan rate will remain constant. Finally, as the date range of this model covers the 1997 Asian Financial Crisis and the 2008 Global Financial Crisis, setting up a dummy variable to test the impact showed that the 1997 Crisis had a more significant impact on the money rate, while the 2008 Crisis had a more significant impact on total output. Even so, when looking at the changes in the economy based on an IS-LM model, it showed that when there is a monetary policy change, the changes in the money rate and output levels are of the same signs as is under the CC-LM model, suggesting that in Taiwan, the IS-LM changes also contradict the theoretical model.
However, as this paper dealt with the overnight money rate as a substitute to the bond rate, it is possible that other close substitutes would show a different effect.
Also, from Bernanke and Blinder’s paper, their money market equation includes a reserve requirement ratio, which can also change the bond rate when the ratio changes. Future studies can place an emphasis on this aspect, or a close substitute to the bond rate to determine how the credit channel operates in Taiwan.
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References
Bernanke, Ben and Alan Blinder, “Credit, Money, and Aggregate Demand.” American Economic Review, May 1988. Vol. 78, 435-439.
Bernanke, Ben and Alan Blinder, “The Federal Funds Rate and the Channels of Monetary Transmission.” American Economic Review, Sept. 1992. Vol. 82, No. 4, 901-921.
Bernanke, Ben and Mark Gertler, “Inside the Black Box: The Credit Channel of Monetary Policy.” Journal of Economic Perspective, Autumn 1995. Vol. 9, No. 4.
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Cottarelli, Carlo and Angeliki Kourelis. “Financial Structure, Bank Lending Rates, and the Transmission Mechanism of Monetary Policy.” International Monetary Fund Staff Papers, Dec. 1994. Vol. 41, No. 4. 587-623.
De Bondt, G.J., 2000. “Financial Structure and Monetary Transmission in Europe.”
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Hannan, Timothy H. and Allen N. Berger. “The Rigidity of Prices: Evidence from the Banking Industry.” American Economic Review, Sept. 1991. Vol. 81, No. 4. 938-945.
Holtemoller, Oliver, “Further VAR Evidence for the Effectiveness of a Credit Channel in Germany.” Applied Economics Quarterly 49, 2003. Vol. 4, 359–381.
Hulsewig, Oliver and Eric Mayer, Timo Wollmerhauser, “Bank Loan Supply and Monetary Policy Transmission in Germany: An Assessment Based on Matching Impulse Responses.” Journal of Banking and Finance, 2006. Vol. 30, 2893-2910.
Klein, Michael A., “A Theory of the Banking Firm.” Journal of Money, Credit, and Banking, May 1971. 205-218.
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129-156.
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Monetarism," Economics Letters, 1983, Vol. 13 (2-3), 167-171.
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何棟欽，2001 年九月，「我國新台幣拆款利率與存、放款利率之關係」。中央銀
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月, 中央銀行經濟研究處, March 2010.
準備貨幣（月底數）(1994) “中華民國台灣地區金融統計月報” 民國八十四年一
月, 中央銀行經濟研究處, March 2010.
準備貨幣（月底數）(1995) “中華民國台灣地區金融統計月報” 民國八十五年一
月, 中央銀行經濟研究處, March 2010.
準備貨幣（月底數）(1996) “中華民國台灣地區金融統計月報” 民國八十六年一
月, 中央銀行經濟研究處, March 2010.
準備貨幣（月底數） (1997) “中華民國台灣地區金融統計月報” 民國八十七年一
月. 中央銀行經濟研究處, March 2010.
準備貨幣（月底數） (1998/6) “中華民國台灣地區金融統計月報” 民國八十八年
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放款利率 (1992-2009), 中央銀行.
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http://win.dgbas.gov.tw/dgbas03/bs7/calendar/calendar.asp?Page=1&Sel-Org=27&S hrField=ShrItm&KeyWrd=存款利率. [June 2010].
國內生產毛額 (1992-2009), 行政院主計處.
http://www.stat.gov.tw/lp.asp?ctNode=2404&CtUnit=1088&BaseDSD=7. [June 2010].
準備貨幣（月底數）(1998/7-2009), 中央銀行經濟研究處.
http://win.dgbas.gov.tw/dgbas03/bs7/calendar/calendar.asp?Page=1&Sel- Org=27&ShrField=ShrItm&KeyWrd=準備貨幣. [June 2010]
拆款利率 (1992-2009), 中央銀行.
http://win.dgbas.gov.tw/dgbas03/bs7/calendar/calendar.asp?Page=1&Sel- Org=27&ShrField=ShrItm&KeyWrd=拆款利率. [March 2010].
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Appendix
A1. Chapter 2: Solving the model
A1.1. Differentiating Loan, Money, and Goods Market
Differentiating equation (2.1) and reorganizing dR to the right hand side, we get:
�Lρ− m(i)R(1 − τ)λρ�dρ + [Li− λ(ρ, i)mi(1 − τ)R − m(i)R(1 − τ)λi]di + Lydy = λ(ρ, i)m(i)(1 − τ)dR ……….. (A1)
Differentiating equation (2.2) and reorganizing dR to the right hand side, we get:
Didi + Dydy = (midi)R + m(i)dR ……… (A2) Differentiating equation (2.3) and reorganizing dR to the right hand side, we get:
−Yidi − Yρdρ + dy = 0 ………. (A3) We reorganize A1, A2, and A3 to obtain the matrix K as described in equation (2.4)
A1.2. Differentiating the CC curve equation
Differentiating equation (2.13) and reorganizing dR to the right hand side, we get:
�1 − Yρρy�dy − �Yi+ Yρρi�di = YρρRdR ………..………. (A4) We use equation (A4) to solve for the slope and shift magnitude of the CC curve.
To solve for ρi, ρy, and ρR we reorganize equation (A1) to get dρ to one side, as shown in equation (A5) solve by differentiating i, y, and R to ρ, which equals equations (2.12a), (2.12b), and (2.12c) respectively.
�Lρ− m(i)R(1 − τ)λρ�dρ = −[Li− λ(ρ, i)mi(1 − τ)R − m(i)R(1 − τ)λi]di − Lydy − λ(ρ, i)m(i)(1 − τ)dR ……….……… (A5)
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A1.3. Solving for Yi
From equation (2.6):
|A| = {λ(ρ, i)m(i)(1 − τ) ∗ (Di− miR) ∗ 1} + {Ly∗ (−Yi) ∗ m(i)}
− {[Li− λ(ρ, i)mi(1 − τ)R − m(i)R(1 − τ)λi] ∗ m(i) ∗ 1}
− {λ(ρ, i)m(i)(1 − τ) ∗ (−Yi) ∗ Dy} ≶ 0 Assuming that |A| = 0, factoring out Yi we can get
−Yi�[Lym(i)� + [λ(ρ, i)m(i)(1 − τ) ∗ Dy]} − {[Li− λ(ρ, i)mi(1 − τ)R − miR1−τλimi+λρ,imi1−τDi−miR=0 ……… (A6)
For the terms {[Li− λ(ρ, i)mi(1 − τ)R − m(i)R(1 − τ)λi]m(i)}, if we use the assumption described in footnote, 2, equation (A6) becomes
−Yi�[Ly∗ m(i)� + [λ(ρ, i) ∗ m(i) ∗ (1 − τ) ∗ Dy]} − {[Li− m(i)R(1 − τ)λi]m(i)} + {λ(ρ, i)m(i)(1 − τ)(Di− miR)} = 0……….. (A6a) From Footnote 5, we can see that
Yi = {−λ(ρ,i)m(i)(1−τ)(Di−miR)+(Li− m(i)R(1−τ)λi)m(i)
Dyλ(ρ,i)m(i)R(1−τ)+m(i)(−Ly) }………..….. (A7) Substituting the complex version of ρR as shown in equation (2.12c), and
reorganizing A7 we can get
Yi�Dyλ(ρ, i)m(i)(1 − τ) − m(i)Ly� − [λ(ρ, i)m(i)(1 − τ)] + [Li− λ(ρ, i)mi(1 − τR−miR1−τλi=0 ………..……….. (A8)
If we multiply out both equation (A6a) and equation (A8), we can see that they are equal to each other, which equals |A|.
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A2. EViews Output of the VAR Model A2.1. No Dummy
Vector Autoregression Estimates Date: 08/23/10 Time: 16:29 Sample (adjusted): 1992Q4 2009Q4 Included observations: 69 after adjustments Standard errors in ( ) & t-statistics in [ ]
GDPSA INTEREST MONEYRATE
GDPSA(-1) 1.302901 1.42E-06 3.56E-06 (0.13358) (3.3E-07) (1.6E-06) [ 9.75349] [ 4.33555] [ 2.28334]
GDPSA(-2) -0.416700 -2.18E-07 3.77E-07 (0.22820) (5.6E-07) (2.7E-06) [-1.82606] [-0.39046] [ 0.14143]
GDPSA(-3) 0.034594 -1.38E-06 -4.41E-06 (0.16090) (3.9E-07) (1.9E-06) [ 0.21500] [-3.49341] [-2.34584]
INTEREST(-1) -6654.268 1.073010 -0.676411 (54168.5) (0.13281) (0.63290) [-0.12284] [ 8.07899] [-1.06875]
INTEREST(-2) -54603.22 -0.130322 1.974232 (78429.7) (0.19230) (0.91637) [-0.69621] [-0.67770] [ 2.15442]
INTEREST(-3) 39249.43 -0.062156 -1.051752 (40329.7) (0.09888) (0.47121) [ 0.97321] [-0.62858] [-2.23203]
MONEYRATE(-1) -1847.986 0.098909 0.664559 (11686.4) (0.02865) (0.13654)
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MONEYRATE(-2) -7310.592 -0.009437 0.180633 (12639.0) (0.03099) (0.14767) [-0.57841] [-0.30453] [ 1.22319]
MONEYRATE(-3) 15995.75 0.015196 -0.146047 (10873.2) (0.02666) (0.12704) [ 1.47112] [ 0.57001] [-1.14961]
C 294608.0 0.449450 -1.969468
(143755.) (0.35247) (1.67962) [ 2.04938] [ 1.27514] [-1.17257]
RMSA 14.27787 0.000108 0.001417
(41.0624) (0.00010) (0.00048) [ 0.34771] [ 1.07730] [ 2.95299]
R-squared 0.994752 0.998599 0.964079
Adj. R-squared 0.993847 0.998357 0.957886 Sum sq. resids 9.84E+10 0.591298 13.42714 S.E. equation 41180.32 0.100969 0.481147
F-statistic 1099.393 4133.342 155.6648
Log likelihood -825.0897 66.29742 -41.43616
Akaike AIC 24.23449 -1.602824 1.519889
Schwarz SC 24.59065 -1.246662 1.876051
Mean dependent 2470900. 6.185072 3.680435 S.D. dependent 524993.9 2.491093 2.344565
Determinant resid covariance (dof adj.) 3366112.
Determinant resid covariance 1999241.
Log likelihood -794.2559
Akaike information criterion 23.97843
Schwarz criterion 25.04692
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A2.2. With Dummy
Vector Autoregression Estimates Date: 08/23/10 Time: 16:30 Sample (adjusted): 1992Q4 2009Q4 Included observations: 69 after adjustments Standard errors in ( ) & t-statistics in [ ]
GDPSA INTEREST MONEYRATE
GDPSA(-1) 1.258583 1.44E-06 3.44E-06 (0.12425) (3.3E-07) (1.4E-06) [ 10.1291] [ 4.31600] [ 2.47090]
GDPSA(-2) -0.250355 -3.00E-07 5.58E-07 (0.21663) (5.8E-07) (2.4E-06) [-1.15570] [-0.51534] [ 0.22991]
GDPSA(-3) -0.052175 -1.34E-06 -4.47E-06 (0.15101) (4.1E-07) (1.7E-06) [-0.34552] [-3.29033] [-2.64081]
INTEREST(-1) 3503.852 1.069625 -0.244987 (50970.3) (0.13700) (0.57139) [ 0.06874] [ 7.80725] [-0.42876]
INTEREST(-2) -47898.20 -0.135177 1.569360 (73136.1) (0.19658) (0.81987) [-0.65492] [-0.68763] [ 1.91416]
INTEREST(-3) 25452.80 -0.055517 -1.100686 (37528.3) (0.10087) (0.42070) [ 0.67823] [-0.55037] [-2.61632]
MONEYRATE(-1) -4157.424 0.099202 0.441085 (11808.4) (0.03174) (0.13237) [-0.35207] [ 3.12548] [ 3.33212]
MONEYRATE(-2) -6780.767 -0.009487 0.236489
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MONEYRATE(-3) 19822.16 0.013849 -0.002626 (10532.5) (0.02831) (0.11807)
Log likelihood -818.5079 66.53741 -31.99847
Akaike AIC 24.10168 -1.551809 1.304303
Schwarz SC 24.52260 -1.130890 1.725222
Mean dependent 2470900. 6.185072 3.680435 S.D. dependent 524993.9 2.491093 2.344565
Determinant resid covariance (dof adj.) 2192633.
Determinant resid covariance 1172149.
Log likelihood -775.8353
Akaike information criterion 23.61842
Schwarz criterion 24.88117
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