HIAS-E-121

Macroeconomic uncertainty matters: A
nonlinear effect of financial volatility onreal economic activity

Jouchi Nakajima (a)(a)

Hitotsubashi University

June 2022

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo 186-8601, Japan
tel:+81 42 580 8668 http://hias.hit-u.ac.jp/
HIAS discussion papers can be downloaded without charge from:https://hdl.handle.net/10086/27202
https://ideas.repec.org/s/hit/hiasdp.html

Macroeconomic uncertainty matters: A nonlinear
eﬀect of ﬁnancial volatility on real economic activity∗
Jouchi Nakajima†

Abstract
A stock market volatility index is a widely-used proxy of uncertainty in themacroeconomy, and its increase is shown to dampen real economic activity. In
contrast, the macroeconomic uncertainty index proposed by Jurado et al. (2015)
measures the predictability of a wide range of macroeconomic indicators and thus
is a comprehensive indicator of macroeconomy-wide uncertainty. This paper empirically investigates a nonlinear link between ﬁnancial volatility and real economic
activity depending on the level of the macroeconomic uncertainty index. Based on
the United States and Japan data, empirical analysis suggests that an increase in
the ﬁnancial volatility lowers industrial production and business ﬁxed investment
more persistently when the macroeconomic uncertainty is higher.
JEL classiﬁcation: E32, E52.
Key words: Financial volatility, Macroeconomic uncertainty, Nonlinear eﬀect.

∗

Financial support from the Ministry of Education, Culture, Sports, Science and Technology of theJapanese Government through Grant-in-Aid for Scientiﬁc Research (No.20H00073) and the Hitotsubashi
Institute for Advanced Study is gratefully acknowledged.
†
Institute of Economic Research, Hitotsubashi University. Address: 2-1 Naka, Kunitachi-city, Tokyo
186-8603, Japan. E-mail: jouchi.nakajima@gmail.com

1

Introduction

A stock market volatility index is extensively used as a proxy of uncertainty in theﬁnancial market. The most popular one is the VIX, conveyed by the Chicago Board
Options Exchange, which measures the 30-day expected volatility of the US stock market,
derived from S&P 500 call and put options. Existing studies often employ the VIX togauge not only the ﬁnancial market uncertainty but also macroeconomy-wide uncertainty,
assuming that the ﬁnancial market uncertainty is correlated with the macroeconomic
uncertainty (see, e.g., Bloom, 2009).
Economic theories suggest that an increase in macroeconomic uncertainty reduces
real economic activity. Bernanke (1983) and McDonald and Siegel (1986) develop thereal option channel in ﬁrms’ investment decisions. Real option value, which is the valueof putting oﬀ a decision of irreversible project such as a business ﬁxed investment, would
increase as the degree of uncertainty rises, and then ﬁrms are more likely to prefer a
wait-and-see strategy for the decision making. Leland (1968) proposes the precautionary saving channel, which suggests that households increase their saving to smooth theirinter-temporal consumption allocations when they expect a rise in the uncertainty of
their future incomes (e.g., Kimball, 1990). Recent studies develop the ﬁnancial frictions
channel (Christiano et al., 2014; Arellano et al., 2019), where a rise in uncertainty increases the default risk of borrowing ﬁrms. Then banks shift the increased risk onto thelending rates, which leads to downward pressure on the ﬁrm’s investments.
On the one hand, the empirical literature exploits the VIX as the indicator of themacroeconomic uncertainty and shows that the increase in the ﬁnancial volatility exacerbates the real economic activity (e.g., Foerster, 2014; Baker et al., 2016; Castelnuovo
et al., 2017). On the other hand, Jurado et al. (2015) propose a novel framework to gauge
macroeconomic uncertainty, namely the macroeconomic uncertainty index. It is designed
to measure the predictability of a wide range of macroeconomic indicators based on atime-series model. Jurado et al. (2015) deﬁne the macroeconomic uncertainty index as
an average of time-varying volatilities across the indicators, which gauge a degree of thereal-time variance of an unexplained component in the ﬂuctuations of the indicators. The
macroeconomic uncertainty index is signiﬁcantly correlated with real economic activity
and thus is a valuable indicator of macroeconomy-wide uncertainty (e.g., Nam et al.,2021; Reif, 2021).

1

Shinohara et al. (2020) show that the ﬁnancial volatility and the macroeconomic
uncertainty index are correlated to some extent, though they are sometimes disconnected.
This evidence suggests that each of the uncertainty indices contains unique informationthat could be related to real economic activity. If so, incorporating both indices into
the empirical model helps us predict the future course of the economy better. However,
little is known about the characteristics of the relationship between ﬁnancial volatility
and macroeconomic uncertainty and their speciﬁc information content.
To ﬁll the gap this study investigates whether the impact of ﬁnancial volatility onreal economic activity depends on the level of macroeconomic uncertainty. Extending
the empirical model that assesses the negative relationship between the ﬁnancial volatility and the real economic activity, the analysis examines whether the macroeconomic
uncertainty index contains additional information about the real economic activity. We
formulate a nonlinear eﬀect of the ﬁnancial volatility whose impact depends on regimesof high and low macroeconomic uncertainty and estimate the eﬀect using data for theUnited States and Japan.
As the ﬁnancial volatility reﬂects market sentiments such as the degree of risk aversion, the stock market generally reacts to news shocks more vividly than the real economic activity. The ﬁnancial volatility also responds to macroeconomic shocks as theymaterialize in the macroeconomic uncertainty. The focus of this paper is whether theinﬂuence of ﬁnancial volatility on the real economic activity depends on the macroeconomic environment, in particular, the degree of uncertainty in terms of the future courseof real economic activity. In a high-level macroeconomic uncertainty regime, the increasein ﬁnancial volatility could aﬀect real economic activity more severely than in a lowlevel uncertainty regime. This paper tests this nonlinear relationship between ﬁnancial
volatility and real economic activity triggered by the level of macroeconomic uncertainty.
As a related study, Jurado et al. (2015) assess diﬀerences between the macroeconomic
uncertainty index and ﬁnancial volatility in terms of time series property and contribution
to explaining the future course of real economic activity. However, the analysis does not
examine the interaction of the two uncertainty indices. From a theoretical perspective,Aı̈t-Sahalia et al. (2021) develop a novel asset pricing model in which the uncertainty inthe stock market return and its volatility are both stochastic, and their degree of connection is also stochastic. In such a situation, the model that takes account of a disconnect2

between volatility and uncertainty signiﬁcantly improves portfolio performance.
The remainder of the paper is organized as follows. Section 2 explains the macroeconomic uncertainty index. Section 3 describes the econometric method and data used in
the empirical analysis for the United States and Japan. Section 4 provides an empiricalanalysis of the nonlinear impact of the ﬁnancial volatility on industrial production andbusiness ﬁxed investment. Section 5 concludes.

2

Macroeconomic uncertainty index

The macroeconomic uncertainty index is based on forecast errors of predicted values
derived by a time series model on various macroeconomic indicators. This index assumes
that economic decision-making depends on how less predictable the economy has become.
When the economy’s future course gets less predictable, we interpret that the economy
becomes more uncertain. Jurado et al. (2015) use 132 monthly indicators collected fromseven categories: production, labor market, consumption, and so on. The index aims
to comprehensively capture the uncertainty caused by various factors underlying themacroeconomy.
For each macroeconomic indicator, denoted by yjt , for t = 1, . . . , T , where j denotes
the series, one-period ahead uncertainty is represented by the conditional volatility offorecasted error, namely, 
Uj,t+1 = E (yj,t+1 − E [yj,t+1 |It ])2 |It ,(1)

where It is the information set available at period t. When the squared error in forecasting
yj,t+1 rises, uncertainty of this macroeconomic indicator increases. To construct theuncertainty index for macroeconomy overall, denoted by MUt , we simply take the averageof the conditional volatilities for all the macroeconomic indicator, as
1 
MUt =
Uj,t+1 ,N j=1Nwhere N is the number of macroeconomic indicators. Jurado et al. (2015) generalize theidea to an h-period ahead uncertainty for h = 1, 2, . . .. In the current paper, we use the3index with h = 1 for simplicity.
To obtain estimates of equation (1), we develop a time-series forecasting model where
each macroeconomic indicator is forecasted using information from the histories of all themacroeconomic indicators available in the analysis. We extract common factors from themacroeconomic indicators and develop a linear regression whose explanatory variables
are the common factors and own lags of the indicator. Then given the forecasted errors
computed from the forecasting model, we estimate a stochastic volatility model to obtaintime-varying volatility in the errors of the one-period ahead forecasting. The stochastic
volatility model has been widely used in ﬁnancial econometrics (e.g., Shephard, 2005) andrecently in macroeconometrics (e.g., Primiceri, 2005; Stock and Watson, 2007). Using theestimated stochastic volatility and parameters in the model, we compute the estimate ofequation (1).
The macroeconomic uncertainty index for the United States is downloadable on Sydney Ludvigson’s website. For Japan, Shinohara et al. (2020) propose a Japan’s macroeconomic uncertainty index following the method of Jurado et al. (2015). The current paper
slightly modiﬁes Japan’s dataset due to the availability of data. The detail of computing
the macroeconomic uncertainty index for Japan is described in Appendix.

3

Empirical model and data

3.1

Local projection methods

We use the local projection methods (Jordà, 2005) to estimate the nonlinear impact of
the ﬁnancial volatility on real economic activity. The empirical model is given by
yt+h = αh + φh yt−1 + βh Vt + γh Vt × I[Mt > M̄ ] + δ h xt−1 + εht ,(2)

for h = 0, 1, . . . , H, where yt is the variable of real economic activity. In the following
empirical analysis, we focus on industrial production and the business ﬁxed investment as
the variable yt . For the uncertainty indices, Vt denotes the index for ﬁnancial volatility, Mt
denotes the macroeconomic uncertainty index, M̄ is a threshold for the macroeconomic
uncertainty index, which generates the nonlinearity of the relationship between yt+h and
Vt . I[ · ] denotes the indicator function that takes the value of one when the argument4

is true and zero otherwise. xt−1 is the vector of explanatory variables other than theuncertainty index. The frequency of the model is monthly for the analysis of industrial
production and quarterly for the business ﬁxed investment.
The coeﬃcient βh measures the impact of the ﬁnancial volatility on the real economic
activity when the macroeconomic uncertainty is low. The coeﬃcient γh assesses an additional eﬀect of the ﬁnancial volatility that arises when the macroeconomic uncertainty
becomes higher than the threshold. We set M̄ as the time-series mean of Mt . The linear
regression is estimated by the ordinary least squares for each horizon h.

3.2

Data

We use the VIX for the United States for the ﬁnancial volatility, downloaded from FREDof the St. Louis Fed. The series is originally provided by Chicago Board Options Exchange (CBOE). For Japan, we use the Volatility Index for Japan (VXJ) provided by
the VXJ research group at the Center for Mathematical Modeling and Data Science,
Osaka University. The VXJ is the implied volatility of the stock price index Nikkei 225.
We convert the daily series of these stock market volatility indices to monthly series bytaking a simple daily average.
For the ﬁnancial volatility indices and the macroeconomic uncertainty indices, we
divide the series by its standard deviation. This standardization makes the interpretationof the estimates easier in the following analysis. In this setting, the coeﬃcient of the
ﬁnancial volatility in equation (2) indicates the impact of a one-standard-deviation change
in the ﬁnancial volatility on the real economic activity.
We use the total index of industrial production, seasonally adjusted. We use thereal gross private domestic investment for the business ﬁxed investment, which is alsoseasonally adjusted. We use the stock price indices S&P 500 and TOPIX (Tokyo Stock
Price Index) for the explanatory variable in analyzing the business ﬁxed investment for
the United States and Japan, respectively. The stock price is employed as is consistent
with Tobin’s Q theory as in the previous studies. We take the natural logarithm for thesevariables in the estimation. We take a three-month average to convert all the variables
explained here to the quarterly series in the analysis of the business ﬁxed investment.
The sample period is from January 1998 to December 2021. We set this period because5the VXJ is available only from January 1998. Because the variables are considerably
volatile during the spread of COVID-19 in 2020, the baseline result is obtained on thesample period up to December 2019. The robustness of the result is examined using thedata extended to December 2021.

4

Empirical analysis

4.1

The uncertainty indicesFigure 1 plots the monthly series of the stock market volatility and the macroeconomic
uncertainty indices (hereafter, MU) for the United States and Japan. Overall, both
series look similar, while each has several notable ﬂuctuations. For the United States,
the VIX and the MU hiked simultaneously around 2008–2009 when the global ﬁnancial
crisis (GFC) hit the economy. Also, they both increased remarkably in 2020 due to the
spread of COVID-19. In contrast, the VIX rose multiple times in the early 2000s and theearly 2010s, while the MU stayed at a low level except for the increase in 2001, reﬂecting
the turmoil when the IT bubble busted. In 2020, it is notable that the VIX decreased
quickly after its peak, while the MU further increased and peaked later.
For Japan, the correlation between the two uncertainty indices is lower than that for
the United States, as pointed out by Shinohara et al. (2020). The VXJ and the MU
rose markedly around 2008–2009 and 2020 in the same manner as the United States.
Still, we observe several notable increases in the MU, reﬂecting, for example, the massive
earthquake in 2011 and the increasing trade tension between the United States and China
in 2019.
We test whether two uncertainty indices have a lag and lead relationship by computing
the time-lagged correlation coeﬃcient between two series, as reported in Figure 2. The
correlation peaks at the simultaneous relation for both the countries, which indicates thatthe two indices do not have a signiﬁcant lag and lead relationship. While the correlations
that the MU leads the VIX are slightly higher than those that the VIX leads the MU,
their diﬀerence is not statistically signiﬁcant at the 5% signiﬁcance level.

6Figure 1: Stock market volatility (VIX for the United States and VXJ for Japan) andthe macroeconomic uncertainty index (MU).
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Nonlinear eﬀect of the ﬁnancial volatility

First, we estimate equation (2) with the monthly series of industrial production, excluding
the nonlinear term of the coeﬃcient γh . For simplicity, no explanatory variable otherthan the uncertainty index is considered. Figure 3 plots the estimation result showing
the estimates of coeﬃcient βh for h = 0, . . . , 48 with its 95% conﬁdence intervals. The
estimate, which measures the impact of the ﬁnancial volatility on industrial production,is signiﬁcantly negative for both the United States and Japan.
Next, we estimate the model including the nonlinear term in equation (2). Figure 4
plots the estimated impact of the ﬁnancial volatility when the macroeconomic uncertainty
is low and the result of the additional eﬀect when the macroeconomic uncertainty is high.
The estimates show that the low-regime impact is short-lived as the response reaches its
bottom around 4–13 months, as observed in the preliminary result above. In contrast,
the latter impact is remarkably persistent as the estimate is signiﬁcantly negative evenafter two years. Notably, this ﬁnding is obtained for both countries. The result implies
that when the macroeconomic uncertainty is high, the additional impact of the ﬁnancial

8

Figure 3: Estimated impacts of the ﬁnancial volatility on industrial production. The
dotted lines indicate the 95% credible intervals. The horizontal axis refers to months.
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volatility on industrial production is negative and considerably persistent.
The quarterly series of the business ﬁxed investment is examined similarly. Industrial
production and stock price are incorporated as the explanatory variables. Figure 5 plots
the result of local projection without the nonlinear term. The rise in ﬁnancial volatility
signiﬁcantly dampens the business ﬁxed investment for both countries. The response of
the investment reaches its bottom around 6–9 quarters.
Figure 6 shows the estimates with the nonlinear term. The impact of the ﬁnancial
volatility is more persistent in the regime of high macroeconomic uncertainty than thatin the low uncertainty regime. The response in the high uncertainty regime reaches
its bottom around 10–12 quarters, while that in the low uncertainty does around 4–7
quarters. Remarkably, this ﬁnding is obtained in both countries.

4.3

Robustness

We conduct robustness checks regarding the sample period, using the empirical model forthe monthly industrial production series. Note that the sample period for the baseline
result is obtained from January 1998 to December 2019. First, we extend the sample up
to December 2021, including the period of the spread of COVID-19. Second, we limit

9Figure 4: Estimated nonlinear impacts of the ﬁnancial volatility on industrial production.
The solid (blue) and bold (red) lines are the impacts when the macroeconomic uncertainty
is low and high, respectively. The dotted lines and the shaded area indicate the 95%
credible intervals. The horizontal axis refers to months.
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the sample period to the late 2000s and the 2010s: speciﬁcally, using the sample period
from January 2007 to December 2019.
Figure 7 shows the estimation result for the diﬀerent sample periods. While only
the mean estimates are plotted without conﬁdence intervals in the ﬁgure, we ﬁnd thatthe impact of ﬁnancial volatility on industrial production is statistically signiﬁcant in all
the cases. The result suggests that the diﬀerence between the low and high regimes ofmacroeconomic uncertainty is evident: the mean response in the high uncertainty regime
is more persistent than in the low uncertainty regime. We conﬁrm the baseline result isrobust for both countries.

10Figure 5: Estimated impacts of the ﬁnancial volatility on the business ﬁxed investment.
The dotted lines indicate the 95% credible intervals. The horizontal axis refers to quarters.
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Figure 6: Estimated nonlinear impacts of the ﬁnancial volatility on the business ﬁxed
investment. The solid (blue) and bold (red) lines are the impacts when the macroeconomic uncertainty is low and high, respectively. The dotted lines and the shaded area
indicate the 95% credible intervals. The horizontal axis refers to quarters.
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industrial production. (i) The bold line is the mean estimate from the baseline sample
period, from January 1998 to December 2019; (ii) the solid line with circles is the estimatebased on data up to December 2021; (iii) the dotted line with crosses is from January
2007 to December 2019. The horizontal axis refers to months.
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5

Concluding remarks

This paper investigates the nonlinear impact of ﬁnancial volatility on real economic activity, which depends on the level of macroeconomic uncertainty. Using the United States
and Japan data, the nonlinear eﬀect is assessed by the local projection methods. The
response of industrial production and the business ﬁxed investment to the rise in thestock market volatility is signiﬁcantly negative. This response appears to be more persistent when the macroeconomic uncertainty is high than that when the macroeconomic
uncertainty is low.
In the literature, other uncertainty indices such as the economic policy uncertainty
index (EPU) of Baker et al. (2016) and those based on disagreements among professional
forecasts on the economic indicators (e.g., Lahiri and Sheng, 2010; Bachmann et al.,2013) have attracted attention. It is of interest to explore what unique information these
indicators have in terms of the impact on real economic activity relative to the stock
market volatility and macroeconomic uncertainty index. This leaves as future work.

13

Appendix. Computation method for the macroeconomic uncertainty index for Japan
Jurado et al. (2015) propose the macroeconomic uncertainty index (MU) for the UnitedStates. This appendix documents the procedure to compute Japan’s MU.
Shinohara et al. (2020) ﬁrstly develop Japan’s MU, following Jurado et al. (2015),
by collecting as many as 67 series of Japan’s macroeconomic variables in the spirit ofJurado et al. (2015): the series are all monthly and available for an extended period(since the 1970s). Also, the dataset covers a wide range of economic activities. In thecurrent study, the dataset is slightly modiﬁed to obtain 60 series, reﬂecting current data
availability. We ﬁnd little diﬀerence between the MU estimated with this new dataset
and the one with the original dataset. The new series does not qualitatively change theempirical results in Shinohara et al. (2020).
Table 1 lists the variables used for the MU for Japan. All the series except theﬁnancial variables No. 54–59 in the list are seasonally adjusted. We take the ﬁrst difference, i.e., changes from the previous month, for the call rate and the 10-year government bond yield, while taking the ﬁrst diﬀerence of the natural logarithm for theother variables. The dataset starts in January 1978, and the estimated MU is available from June 1979. The latest MU series is downloadable at the author’s website:
https://sites.google.com/site/jnakajimaweb/mu.
To compute the MU, we ﬁrst extract common factors of the variables in the dataset.
We standardize each series so that its mean is zero and its variance is one. Deﬁne
Xt = (y1t , . . . , yN t ) as the standardized series, where N denotes the number of series.
Consider a factor model
Xt = Λ F F t + eXt ,where Ft is the r×1 vector of the common factors; ΛF is the N ×r matrix of factor loading;and eXt is the N × 1 vector of idiosyncratic errors. We estimate Ft by the method ofstatic principal component analysis (PCA). We set r = 4 because the contribution of theﬁfth factor is quite small for Japan’s variables. Deﬁne F̂t = (F̂1t , . . . , F̂rt ) as estimatedfactors.

14

To capture a possible non-linearity in the economic agents’ forecasting for macroeconomic variables, we use a squared series of the ﬁrst common factor, i.e., F̂1t2 . In addition,
the ﬁrst common factor for the squares of the indicators, denoted by Ĝt , is also extracted
and incorporated into the model. We deﬁne Ŵt = (F̂1t2 , Ĝt ) .
We estimate the following linear time-series model that contains the common factors
and own lags of the economic indicator as explanatory variables:
yjt =

py

k=1

φjk yj,t−k +pF
FF̂t−k +
γjk

k=1

pW


W
Ŵt−k + vjt ,
γjk

k=1

F
W
where γjkand γjkare 1 × r and 1 × 2 vectors, respectively. The coeﬃcients are estimatedby the least squares method, for j = 1 . . . , N .
Deﬁne v̂jt as the estimated residual of equation (3). This residual can be regarded
as a proxy of one-step-ahead prediction error of yjt conditional on (X1 , . . . , Xt−1 ). We
assume that v̂jt follows the standard stochastic volatility (SV) model:v̂jt = exp(hjt /2)εjt ,hj,t+1 = αj + βj hjt + ηjt ,  

εjt
0
1 0
∼ N
,
,
ηjt
0
0 τj2
where hjt is the log-volatility, hj0 = 0 and ηj0 ∼ N (0, τj2 /(1 − βj2 )). We estimate
the SV model using the STOCHVOL package in R, developed by Kastner and FrühwirthSchnatter (2014). It implements the Markov chain Monte Carlo (MCMC) method withthe ancillarity-suﬃciency interweaving strategy. We obtain estimates using 10,000 iterations after a burn-in period of 10,000 samples. Deﬁne ĥjt , and (α̂j , β̂j , τ̂j ) as the estimatedlog-volatility and model parameters, respectively.
We regard the one-step-ahead expected log-volatility, Et [exp(hj,t+1 )], as a proxy of
the time-varying uncertainty in the one-step-ahead forecasting of yj,t+1 . Denote theuncertainty by Uj,t+1 . Using the estimates of the SV model, we compute this uncertainty

15

measure as
Uj,t+1 = exp α̂j +τ̂j2+ β̂j ĥjt .
2

Finally, we obtain the MU by computing a simple average of the estimated uncertainty
measures for all the economic indicators in the data, as1 
MUt+1 =
N j=1
N

Uj,t+1 .

It should be noted that the index is computed using the estimates of SV parameters
based on the observations for the whole sample period. This approach implies that theresulting index is not an exact estimate of forecasting uncertainty conditional only on
its history because the index uses future information. Also, note that the MU is nota ﬁltered estimate of the stochastic volatility but a smoothed one. Partly due to thistreatment, ﬁgures in the recent period could have signiﬁcant revisions when the series iscomputed with an extended data sample.

16

Table 1: Data for the Japan’s MU.
(I) Production
1 Indices of industrial production (IIP): Total
2 IIP: Capital goods excluding transport equipment
3 IIP: Durable consumer goods
4 IIP: Non-durable consumer goods
5 IIP: Intermediate goods
6 IIP Shipments: Total
7 IIP Shipments: Capital goods excluding transport equipment
8 IIP Shipments: Durable consumer goods
9 IIP Shipments: Non-durable consumer goods
10 IIP Shipments: Intermediate goods
11 IIP Inventory: Total
12 IIP Inventory: Capital goods excluding transport equipment
13 IIP Inventory: Durable consumer goods
14 IIP Inventory: Non-durable consumer goods
15 IIP Inventory: Intermediate goods
16 Indices of tertiary industry activity(II) Labor market
17 The number of new job openings
18 The number of new job applications
19 The number of eﬀective job openings
20 The number of eﬀective job seekers
21 The number of new hires: Establishment with 30 or more employees
22 The number of total separations: Establishment with 30 or more employees
23 Total hours worked: Establishment with 30 or more employees
24 Total cash earnings: Establishment with 30 or more employees
25 The number of employment
26 The number of unemployment(III) Trade
27 Value of exports
28 Value of imports
29 Value of exports to the United States
30 Value of exports to China
31 Real exports
32 Real imports

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Table 1: Data for the Japan’s MU (cont.).
(IV) Consumption
33 Retail sales: Total
34 Retail sales: Department stores and supermarkets
35 New car registration
36 Household spending(V) Investment
37 Construction starts: Floor space, private sector, non-residential
38 Construction starts: Estimated cost of construction, private sector, non-residential
39 New housing starts: Total
40 New housing starts: Owned
41 New housing starts: Rented
42 New housing starts: Built for sale(VI) Prices
43 Consumer price index (CPI): All items
44 CPI: All items, less fresh food
45 CPI: All items, less fresh food and energy
46 Corporate goods price index (CGPI): All items
47 Import price index (IPI): All items
48 IPI: Beverages & foods and agriculture products for food
49 IPI: Textiles
50 IPI: Metals & related products
51 IPI: Lumber & wood products and forest products
52 IPI: Petroleum, coal & natural gas
53 IPI: Chemicals & related products(VII) Financial markets
54 Call rate: overnight
55 Government bond yields: 10-year maturity
56 USD/JPY exchange rate
57 Nominal eﬀective exchange rate
58 Real eﬀective exchange rate
59 Stock price: TOPIX
60 Money supply: M218

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