Trends and Cycles in China’s Macroeconomy Chun Chang Daniel F. Waggoner Tao Zha

Trends and Cycles in China’s Macroeconomy1
Chun Changa
Daniel F. Waggonerb
Tao Zhac
a SAIF,
Shanghai Jiao Tong University
Reserve Bank of Atlanta
c FRB Atlanta, Emory University, and NBER
b Federal
University of Chicago
November 13, 2014
1
c 2012-2014 by Chang, Waggoner, and Zha. The views expressed
Copyright
herein are those of the authors and do not necessarily reflect the views of the Federal
Reserve Bank of Atlanta or the Federal Reserve System or the National Bureau of
Economic Research.
Table of Contents
Overview
Data
Empirical method
Empirical evidence
Conclusion
Introduction
I
For the past two decades Chinas economy has grown rapidly.
Introduction
I
For the past two decades Chinas economy has grown rapidly.
I
Growth in investment has recently fallen because of excess
capacity.
Introduction
I
For the past two decades Chinas economy has grown rapidly.
I
Growth in investment has recently fallen because of excess
capacity.
I
The common view is that business cycles are unimportant in
China and growth is driven entirely by trend.
Introduction
I
For the past two decades Chinas economy has grown rapidly.
I
Growth in investment has recently fallen because of excess
capacity.
I
The common view is that business cycles are unimportant in
China and growth is driven entirely by trend.
I
Yet there has been little empirical evidence to support this
view.
Introduction
I
For the past two decades Chinas economy has grown rapidly.
I
Growth in investment has recently fallen because of excess
capacity.
I
The common view is that business cycles are unimportant in
China and growth is driven entirely by trend.
I
Yet there has been little empirical evidence to support this
view.
In particular there has been little empirical study on
I
I
I
the basic facts on trends, cycles, and volatilities of China’s
economy,
and how various monetary instruments interact with the real
variables.
Introduction
I
For the past two decades Chinas economy has grown rapidly.
I
Growth in investment has recently fallen because of excess
capacity.
I
The common view is that business cycles are unimportant in
China and growth is driven entirely by trend.
I
Yet there has been little empirical evidence to support this
view.
In particular there has been little empirical study on
I
I
I
I
the basic facts on trends, cycles, and volatilities of China’s
economy,
and how various monetary instruments interact with the real
variables.
This paper is to fill this important vacuum by providing some
empirical facts on China’s macroeconomy.
What does this paper do?
The first challenging task is to construct a set of core
macroeconomic time series for China to be as consistent with the
NIPA as possible, all in level on quarterly frequency from
1991Q1 to present (2013Q2):
I nominal and real GDP (fixed-weight – the 1993 UN
standards), implicit GDP deflator;
I CPI, nominal retail consumption (no data available for some
services);
I nominal investment, prices of investment goods;
I nominal exports and imports of goods (these data are
collected by customs, so no data available for services);
I nominal exchange rate for the RMB and the USD;
I nominal government spendings;
I M2, monetary base;
I Required reserve ratio;
I Deposit rates.
Table of Contents
Overview
Data
Empirical method
Empirical evidence
Conclusion
Sources of the data
I
Our quarterly macroeconomic series are constructed based on
the CEIC (China Economic Information Center, now belonging
to Euromoney Institutional Investor Company) Database—one
of the most comprehensive macroeconomic data sources for
China.
I
Two major sources of the CEIC Database are the National
Bureau of Statistics (NBS) and the People’s Bank of China
(PBC).
I
During the past one and a half years, we have been
assembling the macroeconomic time series and are still in the
process of improving our data quality.
Challenge
The difficulty of constructing a standard set of quarterly time series lies
in several dimensions.
I The NBS—the most authoritative source of economic data—reports
only percentage changes of certain key macroeconomic variables
such as real GDP.
I Many variables, such as investment and consumption, do not have
quarterly data. Annual books published by the NBS, using the
expenditure approach, have only annual data with continual
revisions of the data from 2000 on.
I For quarterly or monthly frequencies, there are data published by
the NBS, using the value-added approach (Brandt and Zhu 2010),
for only subcomponents or variables with definitions different from
those with the NIPA expenditure approach.
I Many series on quarterly frequencies are not available for the period
of the early 1990s. For that period, we extrapolate the series that
are likely to be unreliable.
I Few seasonally adjusted data are provided by the NBS or by the
PBC.
Illustration: Construction of Real GDP in Level
I
The NBS publishes y/y changes of real GDP in two forms (let t be
the first quarter of the base year):
yt+1 +yt
yt
I year-to-date (YTD) y/y changes:
yt−4 (Q1), yt−3 +yt−4 (Q2),
yt+2 +yt+1 +yt
yt−2 +yt−3 +yt−4
yt+3 +yt+2 +yt+1 +yt
(Q3), yt−1
+yt−2 +yt−3 +yt−4 (Q4);
yt+1
yt+2
yt+3
yt
I non-YTD y/y changes:
yt−4 (Q1), yt−3 (Q2), yt−2 (Q3), yt−1
(Q4).
I What is published: the data on non-YTD y/y changes go back to
only 1999Q4, while the series of YTD y/y changes begins from
1991Q4 on.
I Given yt (the nominal GDP at quarter t), we obtain yt+4 from
yt+4 = at+4 yt = bt+4 yt ,
where a represents YTD y/y changes and b represents non-YTD
y/y changes.
I We then obtain yt+1 and yt+5 by solving the following two equations
yt+5 + yt+4
= at+5 ,
yt+1 + yt
yt+5
= bt+5 .
yt+1
Illustration: Construction of real GDP in level
I
Now given yt , yt+1 , yt+4 , yt+5 , we obtain yt+2 and yt+6 by solving
the two equations
yt+6 + yt+5 + yt+4
yt+2 + yt+1 + yt
yt+6
yt+2
I
=
at+6 ,
=
bt+6 .
Then, we obtain yt+3 and yt+7 by solving
yt+7 + yt+6 + yt+5 + yt+4
yt+3 + yt+2 + yt+1 + yt
yt+7
yt+3
I
= at+7 ,
= bt+7 .
Given yt , yt+1 , yt+2 , yt+3 , yt+4 , yt+5 , yt+6 , yt+7 , we use the YTD y/y
changes to calculate out all the series of real GDP in level.
Quality of constructed data
We choose the base year (2008Q1) to minimize the differences
between the non-YTD y/y changes implied by our constructed
level series and those from the NBS.
18
16
YTD GDP growth: raw data
non−YTD GDP growth: raw data
non−YTD GDP growth: constracted data
Percent
14
12
10
8
6
1992Q1
1997Q1
2002Q1
Year/Quarter
2007Q1
2012Q1
Inflation data
The implicit GDP deflator is more volatile than the CPI (consistent
with Nakamura, Steinsson, and Liu (2013)).
Inflation (annualized %)
50
GDP
CPI
40
30
20
10
0
−10
−20
1990
1995
2000
2005
2010
2015
Constructed quarterly core data for estimation
Log implicit GDP deflator
Log M2
5
12
4.8
11
4.6
10
4.4
9
4.2
4
8
7
1990
3.8
1995
2000
2005
2010
2015
3.6
1990
1995
Log real investment
8.5
8
8
7.5
7.5
7
7
6.5
6.5
6
6
5.5
1995
2000
2005
2010
2015
5
1990
1995
Log real imports
2010
2015
2000
2005
2010
2015
2010
2015
Log real GDP
8
5.5
7.5
5
7
4.5
6.5
4
6
3.5
5.5
5
1990
2005
Log real exports
9
8.5
5.5
1990
2000
3
1995
2000
2005
2010
2015
2.5
1990
1995
2000
2005
Growth rates (y/y, %)
M2 growth (y/y)
Inflation (y/y)
45
25
40
20
35
15
30
10
25
5
20
0
15
10
1990
1995
2000
2005
2010
2015
−5
1990
Investment growth (y/y)
1995
2000
2005
2010
2015
Real exports growth (y/y)
60
80
60
40
40
20
20
0
0
−20
−40
1990
−20
1995
2000
2005
2010
2015
−40
1990
1995
Real imports growth (y/y)
60
16
40
14
20
12
0
10
−20
−40
1990
2000
2005
2010
2015
2010
2015
GDP growth (y/y)
8
1995
2000
2005
2010
2015
6
1990
1995
2000
2005
Further data analysis
Inflation (y/y, %)
Inflation (q/q, %)
30
50
GDP
CPI
25
20
30
15
20
10
10
5
0
0
−10
−5
1990
1995
2000
2005
2010
2015
−20
1990
Money growth (y/y, %)
1995
2000
2005
2010
2015
Real exchange rate (RMB/$)
50
11
M2
Base
40
10
30
9
20
8
10
7
0
−10
1990
GDP
CPI
40
6
1995
2000
2005
2010
2015
5
1990
3−month deposit rate (%)
1995
2000
2005
2010
2015
Reserve requirement (%)
7
25
6
20
5
4
15
3
10
2
1
1990
1995
2000
2005
2010
2015
5
1990
1995
2000
2005
2010
2015
Further data analysis
Ratio of investment to GDP (%)
Ratio of government spending to total investment (%)
60
50
55
45
50
40
45
35
40
35
30
30
25
25
20
1990
1995
2000
2005
2010
2015
20
1990
Ratios of exports and imports to GDP (%)
28
2000
2005
2010
2015
Ratio of net exports to GDP (%)
7
Exports
Imports
26
6
24
5
22
4
20
3
18
2
16
1
14
0
12
−1
10
1990
1995
1995
2000
2005
2010
2015
−2
1990
1995
2000
2005
2010
2015
Table of Contents
Overview
Data
Empirical method
Empirical evidence
Conclusion
Regimes switching
Dates of switching
December 1978
Beginning of 1992
Early 1990s
January 1994
1994
1995-1996
July 1997
November 1997
November 2001
July 2005
September 2008
2009-2010
Major events
Introduction of economic reforms
Advanced the reforms by Deng Xiaoping
Price controls and rationing
Ended the two-tiered foreign exchange system
Major tax reforms and devaluation of RMB
Phased out out price controls and rationing
Asian financial crises started in Thailand
Began privatization
Joined the WTO and trade liberalization
Ended an explicit peg to the USD
U.S. and world wide financial crisis
Fiscal stimulus of 4 trillion RMB investment
An important role of monetary aggregates in China
I
The PBC heavily regulates lending operations (lending rates,
credit quotas, and “window guidance”) of the four largest
commercial banks: Bank of China, Agricultural Bank of
China, Industrial and Commercial Bank of China, and
Construction Bank of China.
I
With underdevelopment of other forms of financial
intermediation, bank loans remain the major source of funding
for Chinese domestic firms.
I
Thus, broad monetary aggregates such as M2 represent a
good approximation to the central bank’s policy tool as well as
a financial intermediation in the transition of China’s economy.
An important role of reserve requirement in China
I
Interest rates in the money market have been heavily
regulated by the PBC—in essence, no money market has
existed until recently.
I
The required reserves are used by the PBC to influence the
changes in money supply.
Question
I
With all the major political and economic events, as well as
many other such events on a smaller scale, how do we know
which event affects cycles, which event affects volatilities, and
which event triggers trend break?
I
Chow (2002) and Lin (2011) offer an informative narrative of
how the PBC used money supply as a main tool to stabilize
the cycles by expanding or cooling economic growth.
I
The question is how to decouple cycles from trends and
volatilities.
Econometric approach
Built on the VAR framework—Sims and Zha (1998), Christiano,
Eichenbaum, and Evans (1999), and Waggoner, Sims, and Zha
(2009).
The model
I
P
0 A + ε0 Ξ−1 (s ∗ ).
Primitive form: yt0 A0 = c st† + p`=1 yt−`
`
t
t
I
The dimension of yt is n.
I
I
I
I
I
I
I
The switching processes represented by st† and st∗ are
independent of each other.
Regimes: st† ∈ {1, . . . , h† }, st∗ ∈ {1, . . . , h∗ }.
P
0 B + u0 .
Reduced form: yt0 = c̃ st† + p`=1 yt−`
`
t
−1
0
0 −1 (s ∗ ) A−1 .
c̃ st† = c st† A−1
t
0 , B` = A` A0 , ut = εt Ξ
0
The covariance matrix for
−1the reduced-form residuals ut is
.
Σ (st∗ ) = A0 Ξ2 (st∗ ) A00
Companion form: xt = C st† + Bxt−1 + ũt .
The dimension
of xt is m × 1, where m = np. The dimension
†
of C st is m × 1. The dimension of B is m × m. The
dimension of ũt is m × 1.
Imposing the trends
I
Following Hansen, Heaton, and Li (2008), we’ll impose
conintegration relationships on the model and compute the
marginal likelihood to see the fit.
I
In sharp contrast to the HP filter, it allows us to compare the
long-run behavior of the model to the VAR evidence.
Separating trends from cycles
I
I
Decompose B into eigenvalues and eigenvectors:


d1 0
0
−1


,
B = UDU −1 = u1 , . . . , um  0 . . . 0  u1 , . . . , um
0 0 dm
where d1 ≥ · · · ≥ dm .
P
We have xt = m
i=1 αt,i ui , where


αt,1
 .. 
−1
 .  = U xt
αt,m
Separating trends from cycles
I
I
I
Suppose the first q largest eigenvalues are equal to a unit root.
P
The trend component is xtp = qi=1 αt,i ui .
P
The cycle component is xts = m
i=q+1 αt,i ui .
Table of Contents
Overview
Data
Empirical method
Empirical evidence
Conclusion
Model comparison
I
I
With extensive model comparison, data do not favor many of
the changes.
Stochastic switches for volatility regime improve the fit
considerably:
I
I
I
I
Large volatility in 1998Q1-1998Q3 (during the Asian financial
crisis).
Large volatility in 2008Q3-2009Q2 (during the U.S. financial
crisis).
Marginal data density (MDD) in log value: 1.447.9 (no
switching) and 1507.5 (volatility regime).
Data also favor a change in the intercept after the late
volatility regime (2009Q3).
I
MDD in log value: 1508.6.
Estimated volatility regimes
Volatility regime 1
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2008
2010
2012
2014
Volatility regime 2
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1996
1998
2000
2002
2004
2006
Intercept change
GDP
15
10
5
1996
1998
2000
2002
2004
2006
2008
2010
2012
1998
2000
2002
2004
2006
2008
2010
2012
1998
2000
2002
2004
2006
2008
2010
2012
30
M2
25
20
15
10
1996
10
Inflation
5
0
−5
1996
First intercept regime in China—1995Q1-2009Q2
I
The period of 1995Q1-2009Q2 can be characterized as the
period of privatization and trade liberalization.
I
Cheap labor supply, strong worldwide demand for China’s
exports, productivity gains from restructuring the
state-owned-enterprises (SOEs), and reallocation of capital
and labor between SOEs and domestic private enterprises
(DPEs) are the most important aspects of privatization and
trade liberalization during this period.
I
The low-productive SOEs had access to bank loans, while the
high-productive DPEs faced severe external finance
constraints (Bai, Hsieh, and Qian, 2006; Song, Storesletten,
and Zilibotti, 2011; Yang, 2012).
I
In March of 1998, the PBC began to use the RRR as an
important instrument for conducting monetary policy.
Second intercept regime in China—2009Q3-2013Q2
I
In the aftermath of the world wide financial meltdown in
September of 2008, the government put in 4 trillion RMB
investment in 2009 and 2010 as a fiscal stimulus.
I
But the most conspicuous stimulus was given by the PBC
with a sharp increase in M2 growth in 2009-2010 (in contrast
to an increase in the Federal Reserve’s balance sheet).
I
These efforts aimed partly at stabilizing the fluctuations
caused by the financial crisis and partly at stemming the tide
of an inevitable lower growth trend.
I
Thus it is important to extract trend and cyclical components
from the data in one single time-series framework.
Estimated results
I
Decomposition of growth rates (annualized %):
Regime
M2
Inflation
Investment
Exports
Imports
GDP
Data
I
II
16.38 15.48
2.69
4.27
12.25 9.05
11.56 9.90
10.06 10.78
9.36
8.46
Intercept Changes
Trend
I
II
17.93 15.86
3.57
3.15
11.08 8.00
3.34
4.69
6.33
3.63
9.80
9.30
Cycle
I
II
-1.55 -0.38
-0.88 1.11
1.17
1.04
8.22
5.20
3.72
7.14
-0.43 -0.83
I
Cyclical fluctuations contribute to a considerably large portion
of growth changes in prices, exports, and imports.
I
A nontrivial fraction of growth in investment and output is
also drive by cyclical fluctuations.
I
Slower trend growth of output in Regime II is driven by slower
trend growth of both M2 and investment, as well as exports
(see the distribution).
68% error bands for growth rates under intercept changes
M2
Inflation
Investment
Exports
Imports
GDP
M2
Inflation
Investment
Exports
Imports
GDP
Low
13.07
2.14
9.39
2.67
6.79
7.34
Regime I—Higher Growth
Trend
Cycle
Estimate
High
Low Estimate
17.93 18.28 -1.96
-1.55
3.57
4.37 -1.70
-0.88
11.08 14.81 -2.61
1.17
3.34 18.34 -6.85
8.22
6.33 19.93 -9.94
3.72
9.80 11.21 -1.85
-0.43
High
3.30
0.54
2.83
8.66
3.18
1.99
Low
10.88
1.66
5.69
-0.38
2.11
5.17
Regime II—Lower Growth
Trend
Cycle
Estimate
High
Low Estimate
15.86 15.56 -0.08
-0.38
3.15
3.63
0.62
1.11
8.00 10.63 -1.58
1.04
4.69 11.16 -1.34
5.20
3.63 13.90 -3.12
7.14
9.30
8.75 -0.29
-0.83
High
4.59
2.60
3.34
10.14
8.59
3.27
Estimated trends
5
12
Price
M2
14
10
8
1995
2000
2005
2010
4
1995
2015
8
7
2005
2010
4
1995
2015
5
5
GDP
Imp
2000
4.5
2000
2005
2010
2015
2000
2005
2010
2015
2000
2005
2010
2015
2000
2005
2010
2015
4
3
1995
2015
0.03
Drate
0.12
RRR
2010
5
6
0.1
0.08
1995
2005
4.5
5.5
4
1995
2000
5.5
Exp
Invest
9
6
1995
4.5
2000
2005
2010
2015
0.025
0.02
0.015
1995
Estimated trend distributions: Exports
0.09
Regime I
Regime II
0.08
0.07
Probability density
0.06
0.05
0.04
0.03
0.02
0.01
0
−20
−10
0
10
Exports
20
30
40
Estimated trend distributions: Investment
0.35
Regime I
Regime II
0.3
Probability density
0.25
0.2
0.15
0.1
0.05
0
0
2
4
6
8
10
Investment
12
14
16
18
20
Estimated trend distributions: GDP
0.35
Regime I
Regime II
0.3
Probability density
0.25
0.2
0.15
0.1
0.05
0
0
5
10
GDP
15
Estimated cycles
0
Price
M2
−0.4
−0.6
−0.8
1995
2000
2005
2010
−0.2
−0.4
1995
2015
0.5
0
1995
2000
2005
2010
GDP
Imp
2
2000
2005
2010
2015
2000
2005
2010
2015
2000
2005
2010
2015
2000
2005
2010
2015
2
−0.2
−0.3
1995
2015
0.04
Drate
0.2
RRR
2010
−0.1
2.5
0
−0.2
1995
2005
3
1
1995
2015
3
1.5
1995
2000
4
Exp
Invest
1
2000
2005
2010
2015
0.02
0
−0.02
1995
Some facts about the cyclical movements
I
The cycles are very long.
I
Prices, GDP, reserve requirement, and the deposit rate tend
to comove.
I
Imports, exports, investment tend to comove.
I
There is little comovement between investment and GDP.
I
The relationships between exports and GDP are weak.
I
Money supply tends to move in opposite with investment and
the external sector (imports and exports) than with GDP.
Correlations over business cycles
M2
Price
Investment
Exports
Imports
GDP
RRR
D Rate
M2
1.0000
0.4016
-0.7759
-0.9794
-0.9602
0.3951
-0.3706
0.4835
Prices
0.4016
1.0000
0.0584
-0.2758
-0.5252
0.9116
0.6828
0.7910
Investment
-0.7759
0.0584
1.0000
0.8485
0.7230
-0.0177
0.6597
-0.2223
Exports
-0.9794
-0.2758
0.8485
1.0000
0.9398
-0.2901
0.4880
-0.4525
Imports
-0.9602
-0.5252
0.7230
0.9398
1.0000
-0.5617
0.2044
-0.5551
GDP
0.3951
0.9116
-0.0177
-0.2901
-0.5617
1.0000
0.6139
0.7135
RRR
-0.3706
0.6828
0.6597
0.4880
0.2044
0.6139
1.0000
0.3631
D Rate
0.4835
0.7910
-0.2223
-0.4525
-0.5551
0.7135
0.3631
1.0000
Table of Contents
Overview
Data
Empirical method
Empirical evidence
Conclusion
Dynamic responses with square root
Drate
RRR
GDP
Imp
Exp
Invest
Price
M2
M2
Price
Invest
Exp
Imp
GDP
RRR
Drate
Dynamic responses withCholeski
Drate
RRR
GDP
Imp
Exp
Invest
Price
M2
M2
Price
Invest
Exp
Imp
GDP
RRR
Drate
Impulse responses to a trend shock
−3
M2
Price
x 10
0.02
7
6
0.018
5
0.016
4
3
0.014
2
0.012
1
0
0.01
−1
0.008
−2
0
5
10
15
20
25
0
5
10
−3
Investment
20
25
15
20
25
GDP
x 10
0.016
15
3.5
0.014
3
0.012
2.5
0.01
0.008
2
0.006
1.5
0.004
1
0.002
0
0.5
0
5
10
15
20
25
0
5
10
Impulse responses to two stationary shocks
−3
2
−3
M2
x 10
1
1
M2
x 10
0
0
−1
−1
−2
0
5
10
−3
2
15
20
25
−2
0
5
10
−4
GDP
x 10
5
15
20
25
15
20
25
GDP
x 10
0
0
−5
−2
−4
−10
0
5
−3
6
x 10
10
15
20
25
−15
2
4
0
0
−1
0
5
−3
1
10
15
20
25
−2
10
Reserve requirement ratio
x 10
0
5
−4
Interest rate
15
0.5
10
0
5
−0.5
−1
5
x 10
1
2
−2
0
−3
Reserve requirement ratio
10
15
20
25
20
25
Interest rate
x 10
0
0
5
10
15
20
25
−5
0
5
10
15
Findings
I
Money supply has been largely endogenous in response to a
trend shock so that M2, the price level, and GDP co-move
(previous figure).
I
In contrast to Leeper, Sims, and Zha (1996) and Christiano,
Eichenbaum, and Evans (1999), the effect on output of a
stationary shock to the reserve requirement ratio is stronger
than that of a stationary shock to the interest rate (previous
figure).
I
Right column of the previous figure: an increase of 0.1% in
the interest rate lowers Chinas GDP by 0.08%, but not very
persistently.
I
Left column of the previous figure: an increase of 0.1% in the
required reserve ratio lowers Chinas GDP by 0.08%, but very
persistently.
Recap
I
Trend inflation appears not to be a problem.
I
Rapid trend growth in investment seems to be a driving force
of impressive trend growth of GDP.
I
Cyclical fluctuations, even after controlling for a switch in
intercept, are important for China’s economy.
I
Cyclical fluctuations of exports and imports move together
with those of investment.
I
Cyclical fluctuations of investment and exports, however, do
not comove with those of GDP (fiscal policy implications).
Leaning-against-wind monetary policy:
I
I
I
M2 moves in opposite with both GDP and inflation over the
business cycle.
The required reserve rate and the deposit rate comove with
both GDP and inflation over the business cycle.
Conclusion
I
I
In the transition of China’s macroeconomy, monetary
aggregates such as M2, as well as required reserves and the
deposit rate, play a substantive role in both fluctuations and
growth of aggregate output.
Moving beyond privatization and trade liberalization, financial
liberalization (as confirmed by the November 2013 plenum of
China) is needed in the future:
I
I
I
Economic growth may face a trend break or a long cycle with
more reliance on an increase of domestic demand
(consumption).
Fluctuations of M2 will inevitably play a less important role in
both growth and cycles as financial reforms go beyond the
banking sector.
Any structural macro model should take into account all these
salient features. Back to Introduction