Discussion Papers In Economics
And Business

Tobin’s q as a transmission channel for nontraditional monetary policy:
The case of Japan

Yuzo Honda and Minoru Tachibana

Discussion Paper 12-16

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANTobin’s q as a transmission channel for nontraditional monetary policy:
The case of Japan

Yuzo Honda and Minoru Tachibana

Discussion Paper 12-16
September 2012

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANTobin’s q as a transmission channel for nontraditional monetary policy: Thecase of Japan

Yuzo Honda
Kansai University
andMinoru Tachibana
Osaka Prefectural University

September 2012
ABSTRACTThe purpose of this paper is to provide objective statistical evidence on the effectivenessof nontraditional monetary policy. The quantitative easing monetary policy, adopted by
the Bank of Japan for the period from March 2001 to March 2006, had a stimulating
effect on investment and production at least through the Tobin’s q channel.

JEL classification: E51

Keywords: Quantitative easing, Stock prices, Newly issued stocks, Investment, Vector
autoregression

Address for correspondence: Yuzo Honda, Kansai University, Takatsuki-shi, Osaka
569-1095, Japan1

1. Introduction
The question of whether nontraditional monetary policy measures have real effects is
controversial and ongoing, largely because there are insufficient data to identify the
impacts of nontraditional monetary policy measures.
The Japanese Economic Association (JEA) organized a panel discussion on“Evaluation of Nontraditional Monetary Policy” at the association’s 2011 Autumn
Meeting, the record of which was published in JEA (2012). Although some panelists
expressed skepticism about the effectiveness of nontraditional monetary policy
measures for the macroeconomy, their assertions were based on no or little empiricalevidence. The purpose of this paper is to show that such claims conflict with statistical
data. Rather, the data suggest that the Bank of Japan’s (BOJ) quantitative easing (QE)monetary policy had significant impacts on production by inducing changes in stock
prices. Furthermore, it seems evident that Tobin’s q (the ratio of market value of
installed capital to replacement cost of capital) was working as a channel through whichmonetary policy shocks were transmitted to investment in the real sector.
2. Vector autoregressions (VARs)
Assessments of the effectiveness of monetary policy usually include variables on
production, prices and the short-term interest rate in a VAR. During the period of QE
from March 2001 to March 2006, however, the BOJ used the total balance of bankreserves (TBBR) as its operating target, because the overnight call rate (the standard
operating target) was stuck at virtually 0%. Hence, if we restrict our sample to theperiod from March 2001 through March 2006, and replace the short-term interest rate2

with TBBR, we can directly apply the standard VAR method to analyze the efficacy of
nontraditional monetary policy during the QE period.
We start with the simplest three-variable VAR model with indices of industrial
production (y), consumer price index (p) and TBBR included in the VAR. With thisthree-variable VAR, the results are clear-cut: TBBR affected production y. The question
then arises regarding the channel by which variation in TBBR affected y. Keeping in
mind the need to be parsimonious in our model specification, we add one variable at atime. As the fourth variable, we tried various options, including government bond rates
of different maturities, stock prices and exchange rates, and found that stock prices hada significant influence on production. Stock prices thus became the fourth variable inour four-variable VAR model. As for the data on TBBR, the results were virtually thesame, whether we used the actually realized balances of bank reserves or the target
numbers officially announced by the BOJ.
The results of the four-variable VAR model indicate that changes in the BOJ’s
TBBR target immediately led to changes in stock prices and then to changes in
production with some time lags.
Results from three analytic tools—Granger causality tests, impulse response
analysis and variance decomposition—unanimously support the above findings. This is
the main result in our first paper (Honda et al., 2007; HKT hereafter). The weakness of
HKT is the small sample size, meaning our findings might not be robust.
To improve statistical inference, in Honda and Tachibana (2011; HT hereafter), we
extended the sample period from March 2001 through March 2006 to January 1996
through March 2010, thus increasing the sample size from 60 to 171. Introducing
dummy variables into the VAR, we fully exploited prior information on structural3

changes of operating targets from the short-term interest rate to TBBR during the QE
period.
We consider three kinds of VAR models in Table 1. Model (i) is the simplest
five-variable VAR, including prices (p), production (y), the overnight call rate (r), stock
prices (s) and the actual TBBR (m), multiplied by the dummy variable (d1), where d1
takes the value 1 during the QE period and 0 otherwise. In ordinary times, the BOJ sets
its operating target to the overnight call rate. However, with the overnight call rate stucknear 0% during the QE period, BOJ used TBBR as its operating target. This is why weinclude the variable TBBR (m) during the QE period only in model (i). In model (i), “c”,
“c1” and “c2” denote (5  1) vectors of intercept parameters.

<Table 1 around here>

Model (ii) is the six-variable VAR, including two dummy variables, d1 and d2,
where the second dummy variable d2 takes the value 0 during the QE period and 1
otherwise. Model (i) has the shortcoming of treating the QE period and ordinary time
periods asymmetrically, because the variable (d1  m) takes values m during the periodof QE and 0 otherwise. With the second dummy variable (d2  m) included, model (ii)treats the QE period and ordinary time periods symmetrically, and allows the variable
TBBR (m) to have different dynamic effects on other variables during the QE period
compared with ordinary time periods. Model (ii) has an advantage over model (i) in that
we can treat the QE period and ordinary time periods symmetrically, although at thecost of losing efficiency of estimation with the increased number of parameters.

4Model (iii) assumes that the effects of TBBR on other variables might differ foreach of three periods, namely, the period of QE, the period before the QE and the period
after the QE. Model (iii) includes three dummy variables, d1, d3 and d4, where d3 takes
the value 1 during the period before the QE only and 0 otherwise, and d4 takes the value
1 during the period after the QE only and 0 otherwise, respectively.
By increasing the sample size from 60 in HKT to 171 in HT, we were able toobtain more precise estimates on the relationships between the macroeconomic
variables, and thereby increase the statistical power of hypothesis testing on theeffectiveness of QE. The qualitative results in HT turned out to be virtually the same asin HKT.
Figure 1 reports the response estimates of prices (p), production (y) and stock
prices (s) to a one standard deviation shock in TBBR (m). The dotted lines indicate the
90% confidence bounds, obtained through Monte Carlo simulations of 500 repetitions.
In all three models, responses in prices (Core CPI) are negligible, but those inproduction (IIP) and stock prices are significantly different from zero. Stock prices react
immediately but production only with some time lags, as expected.

<Figure 1 around here>

HT also reported the estimates of impacts of 1 trillion yen increases in the TBBR
target on production and stock prices. Readers can consult the original paper for details.

5

3. Transmission via the Tobin’s q channel
3.1. Stock prices and newly issued stocks
Changes in stock prices can affect the real sector through four possible channels (seeHT for details). Here we examine only one of these, the Tobin’s q channel, and reportthat it played a crucial role in transmitting financial shocks to the real economy.
3.1.1. Correlation
Figure 2 shows movements in stock prices (Nikkei 225) and the corresponding amountsof newly issued stocks (including convertible bonds and warrants) during the period of
QE (March 2001 through March 2006).

<Figure 2 around here>

In March 2003, the Japanese government injected bank capital of about 1.8 trillion
yen into Resona Bank in a bid to prevent the turmoil from spreading through the entire
Japanese economy. Hence, March 2003 is clearly an outlier in our sample, and we
exclude it from both Figure 1 and our statistical analysis.
In Figure 2, we find a downward trend in stock prices before March 2003, but an
upward trend after that. Comparing the former 24-month period (March 2001 throughFebruary 2003) with the latter 36-month period (April 2003 through March 2006), we
observe the tendency that the amounts of newly issued stocks for the latter period areclearly larger than those for the former period. Indeed, this was the case. The averageamount of newly issued stocks increased from 213 billion yen in the former period to
356 billion yen in the latter period, a rise of 67%. With a t-value of 2.55, we reject the6null hypothesis at the significance level of 1% that the means of the amounts of newly
issued stocks are the same for the two subsamples.
The average stock price (Nikkei 225) increased from 10,699 yen in the former
period to 11,608 yen in the latter, which suggests a positive correlation between stock
prices and the amounts of newly issued stocks. This assertion is supported by further
data: the correlation coefficient turns out to be 0.34 between stock prices and theamounts of newly issued stocks.
3.1.2. Causality from stock prices to newly issued stocks
The data reveal more than that. A comparison of the two subsamples shows that the rateof increase in the average amounts of newly issued stocks, 67%, is far greater than thecorresponding rate of increase in stock prices, 8.5%. This finding agrees with the idea of
Tobin’s q, suggesting causality from stock prices to newly issued stocks. That is, thehigher stock prices induced firms to issue larger amounts of new stocks. Fitting astraight line to the data between stock prices st on the right-hand side and the amountsof newly issued stocks at on the left-hand side, we find the following ordinary leastsquares (OLS) estimates:

at = –188,975 + 43.366 st.
(1.06)(2.77)(1)

The sample period covers from March 2001 through March 2006 with the outlier
sample of March 2003 excluded. Numbers in parentheses in Eq. (1) indicate therespective t-values of the estimates. The t-values indicate that stock prices st affect theamounts of newly issued stocks at at the significance level of 1%, consistent with the7hypothesis that Tobin’s q worked as a transmission channel from the financial to the real
sector.
3.2. Stock prices and fixed investment
Did the increase in newly issued stocks really lead to increased production in the latter
half of the QE period? Only quarterly data are available for fixed investment, and weadmit, at the outset, that it is difficult to answer the above question statistically.
Constructing three months of average data of stock prices, St, and regressing fixed
investment It on stock prices St, we find the following OLS estimates:

It+4 = 12517.13 + 0.42 St.
(5.74)(2.18)(2)

Numbers in parentheses indicate the t-values. The sample period covers from the firstquarter of 2001 through the first quarter of 2006, but the sample size of 21 is too small
to give enough information to identify the effects of other variables. Hence, we should
be cautious when interpreting the estimation results in Eq. (2). Nevertheless, the t-valueof the estimate of stock prices St turns out to be 2.18, and is statistically significant at
the 1% level. This result does not disagree with the hypothesis that higher stock prices
induced firms to issue larger amounts of new stocks, which in turn led to increases in
fixed investment and production.
4. Concluding remarks
Our findings are tentative and only suggestive, as our results are based on BOJ data on
its QE monetary policy, the length of which is limited to about five years. Nevertheless,we believe that it is worth publicizing the objective empirical results of the present8paper, given the pressing nature of the issue that central banks in advanced economies
are currently facing.
Acknowledgments
We are grateful to Masayoshi Amemiya, Yasushi Iwamoto, Yoshihiro Kuroki, Etsuro
Shioji, Kazuyuki Suzuki and Kazuo Ueda for useful discussions, and to Keiko Makino
and Junko Ohnishi for their help with data.
References
Honda, Y., Kuroki, Y., Tachibana, M., 2007. An injection of base money at zero interest
rates: empirical evidence from the Japanese experience 2001–2006. Osaka
University, Discussion Papers in Economics and Business, No. 07-08.
Honda, Y., Tachibana, M., 2011. The financial crisis and the quantitative easing
monetary policy in Japan. Osaka University, Discussion Papers in Economics andBusiness, No. 11-18 (in Japanese).
Japanese Economic Association (JEA), 2012. Evaluation of non-traditional monetary
policy, in Ohgaki, M., Ogawa, K., Konishi, H., Tabuchi, T. (Eds.), Current
Economics 2012. Toyokeizai Shinposha, Tokyo, pp. 193–235 (in Japanese).

9Table 1
VAR models

VAR model (i)Y = (p, y, r, d1  m, s)’, c = c1 + (c2  d1)

VAR model (ii)Y = (p, y, r, d1  m, d2  m, s)’, c = c1 + (c2  d1)

VAR model (iii)Y = (p, y, r, d1  m, d3  m, d4  m, s)’, c = c1 + (c2  d1) + (c3  d4)

Honda and Tachibana (2011)10

Fig. 1. Impulse response analysis

Model (i)Model (ii)

Model (iii)Core CPI

Core CPI

Core CPI

0.03

0.08
0.06
0.04
0.02
0
-0.02
-0.04
-0.06
-0.08

0.04
0.03

0.02

0.02

0.01

0.01
0

0

‐0.01

‐0.01

‐0.02
0

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12

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48

0

6

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IIP

30

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0

48

IIP

0.4

0.8

0.1

0.3

0.6

0.2

0.4

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0.05
0
‐0.05

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6

Stock prices

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Stock prices
3

1.4
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0.8
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48

Stock prices

0.8

0

12 18 24 30 36 42 48

IIP

0.15

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Honda and Tachibana (2011)

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