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www.conferenceboard.org © 2010 The Conference Board, Inc. | 1
Ataman Ozyildirim, The Conference Board
May 10, 2013
On Trends, Cycles, and Turning Points of Emerging
Economies: Examples from China, India, Brazil
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About The Conference Board
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What is The Conference Board?
The Conference Board is a global, independent business-
membership and research association, started in 1916, working in
the public interest.
Our mission is unique: To provide the world’s leading organizations
with the practical knowledge they need to improve their
performance and better serve society.
Our membership includes over 1,200 companies in both the
established and the emerging markets of the world.
Objective economic data and analyses that help business and
policy leaders make sense of their operating environments
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Three pillars underlying the economics program
Short term indicators, forecasting and analysis:
Consumer Confidence Index and CEO Confidence Indicator
Leading Economic Indicators
U.S. economic forecast, Euro Area and global growth projections
Medium to long-term outlook:
Productivity and Competiveness Databases
Intangible Capital and Innovation Research
Global Economic Outlook
Business scenario development:
Macro scenarios for major countries and regions
Starter scenarios for sectors and major economic activities
Business scenarios at firm level
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Overview:
TCB experience
Business cycles: definitions, theories, and indicator
approach
Methodology: applying the indicator approach
Coincident indicators
Leading indicators
Growth cycles vs. business cycles in emerging
economies
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Outline
The need to monitor emerging markets domestic cyclical dynamics
and the indicator approach
Can emerging markets data problems be overcome to produce
reliable metrics to understand the cycle?
Applications for emerging economies from China, India and Brazil:
Data problems – An example: Pitfalls of the Chinese indicators
The business cycle indicators approach
Concluding remarks
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The challenge for economists and forecasters
Governments, business leaders, financial markets, and foreign
investors need reliable, high-frequency, and up-to-date
information about the state of the economy
There are only piecemeal indicators available, but lots of
different models and theories
Data quality is the key issue: consistency, comparability, long
enough history, without gaps, high enough frequency, and
timely publishing
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U.S. LEI transfer from Department of Commerce (Bureau of
Economic Analysis) in 1996
Expansion of BCI portfolio to 11 countries and regions, with
India and Brazil on the way
Modeled after U.S. system of monthly indicators
Economic theory informs selection of indicators, but country
specific features influence choice
Composite indexes help define, emphasize, and predict
cyclical movements
The indicators approach is known for its objectivity,
independence, consistency, and reliability
The Conference Board U.S. and global business cycle
indicators (BCI) program
www.conferenceboard.org © 2010 The Conference Board, Inc. | 9
-30
-25
-20
-15
-10
-5
0
5
10
15
20
25
00 01 02 03 04 05 06 07 08 09 10 11 12 13
U.S. (Mar '13)
Euro Area (Feb '13)
China (Mar '13)
6-month percent change (annual rate)
The Conference Board
Leading Economic Indexes®
The Conference Board Leading Economic Index® track
the business cycle and help predict 3-6 months ahead
Source: The Conference Board
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Introduced indexes for ten new countries since 2000
Australia, China, Euro Area, France, Germany, Japan,
Mexico, South Korea, Spain, United Kingdom
Consistent methodology allows international comparisons
New LEIs under development
Brazil
India
Global Business Cycle Indicators Program (BCI - LEI)
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Short term macroeconomic performance of emerging
markets is increasingly important for the world economy
Importance of reliable macroeconomic performance indices for
policy making, especially those that are able to signal likely
significant changes in direction in the short term
Most of the exercises of constructing “leading economic
indicators” do not consider explicitly what those indicators lead
Indicators approach looks at a coincident index as the cyclical
measure as an alternative to GDP and as monthly target of the
leading indicators
And, we ask whether the leading index can improve forecasts,
evaluate indexes by real time out of sample forecasting
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TCB experience
Business cycles: definitions, theories, and indicator
approach
Methodology: applying the indicator approach
Coincident indicators
Leading indicators
Growth cycles vs. business cycles
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Business cycles defined
“Business cycles are a type of fluctuation found in the aggregate economic
activity of nations that organize their work mainly in business enterprises:
a cycle consists of expansions occurring at about the same time in many
economic activities, followed by similarly general recessions,
contractions, and revivals which merge into the expansion phase of the
next cycle; this sequence of changes is recurrent but not periodic; in
duration business cycles vary from more than one year to ten or twelve
years; they are not divisible into shorter cycles of similar character with
amplitudes approximating their own.”
Wesley C. Mitchell, (1927), Business Cycles: The Problem and Its Setting,
New York, NY: National Bureau of Economic Research.
Burns, A. F., and Mitchell, W. C. (1946), Measuring Business Cycles, New
York, NY: National Bureau of Economic Research.
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A synopsis of selected business cycle theories
Type of Theory
No Hawtrey
1913 - 37
No or weakly Hayek
1931 - 39
Yes Schumpeter
1912 - 39
Frisch 1933
A disequilibrium
theory of
investment and
financial instability
(largely
endogenous)
Unstable expected profits
drive business
investment, which
generates fluctuations in
realized profits
Yes Minsky 1982Money created by bank
lending to business; short-
term financing of long-term
investment
Relative prices of
capital assets set in
financial markets under
uncertainty about future
returns, costs of capital,
and cash flows
Long expansions produce
over-confidence, unsound
financing practices, a
growing debt burden and
illiquidity…sources of
contractions and crises
Friedman and
Schwartz
1963a, 1963b
Market clearing
with rational
expectations and
incomplete
information
Random monetary
shocks causing price-
level variations
No Lucas 1977General price changes
misperceived for relative
price changes; intertemporal
substituion of labor and
leisure
Prompt and strong
reactions to perceived
changes in relative
prices or real rates of
return on the supply
side
Flexible prices and wages
clear markets continuously;
money and price surprises
cause fluctuations in output
and investment
Capital-goods
production, but the
system as a whole is
damped (dynamically
stable)
Random shocks or
innovations bunched in
expansions needed to
maintain oscillations
Yes (through
innovations)
The original
monetarist theory
Sequential shocks: high
monetary growth rates
followed by low rates,
etc.
Relative prices and asset
yields, then spending flows
Both consumption and
investment react to
monetary changes
No Monetary policies
destabilize the private
sector
Cyclical real
growth
Burst of innovation
contested by imitators
Credit financing; excesses of
speculation and
misjudgement
Business capital
investment booms and
readjustments in
contractions
Simultaneous interacting
long, intermediate, and
short cycles
I. Some Largely Endogenous Theories
II. Some Theories with Major Exogenous and Stochastic Elements
Impulse and
propagation in a
real model
Undefined erratic shocks
and discontinuous
Schumpeterian
innovations
Investment accelerator, lags
in output of capital goods,
money demand and
imperfectly elastic supply
Cycles tend to be periodic
under the gold standard
Monetary
disequilibrium
Monetary
overinvestment
Unstable supply of bank
credit
Discrepancy between the
natural and money interest
rates
Capital investment,
production processes
Real vertical
maladjustments result from
monetary disequilibria
Unstable flow of money
(bank credit)
Interest rate changes; cycles
of inflation and deflation
Investment in traders
inventories
Most sensitive
ProcessesResponsiveOriginating
Are Cycles
Linked to
Growth?
Main Factors
Special FeaturesAuthors &
Dates
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Organizing principle: Taxonomy of indicators and tracking
cyclical movements with composite indexes
Empirical theory: There is an underlying but not directly observable
cyclical variable that is inherent in and common to all economic
data (but it is hidden because of the dispersion in turning points)
Composite indexes summarize and emphasize cyclical co-
movements and estimate this underlying common variable
Looking beyond irregular and seasonal movements in data to the
common and regular cyclical fluctuations
Coincident index provides gauge of “current conditions”
– Key to developing new indexes
– Determining turning points (reference chronology)
Leading index anticipates peaks and troughs
Lagging index confirms
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Some terminology
Indicator, composite index
An indicator is a time series variable
A (composite) index is an aggregation of two or more indicators
Diffusion indexes
Leading Economic Index (LEI) and Coincident Economic Index (CEI)
LEI and CEI belong to a category of indexes called diffusion indexes
Indexes measuring balances (percent rising etc.) are also called diffusion
indexes
Trend (structural, long term) vs. cycle (short term, temporary)
Business cycle vs. growth cycle
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Cyclical timing: leading vs. coincident indicators
BCP
BCT
time
CEI
LEI
P
T
Lead (-4)
Lead (-2)
BCP: business cycle peak
BCT: business cycle trough
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Business cycles vs. growth cycles
BCP
BCT
time
time
GCP
GCT
Le
ve
l o
f e
co
no
mic
activity
De
via
tio
n fro
m lo
ng
te
rm tre
nd
BCP: business cycle peak
BCT: business cycle trough
GCP: growth cycle peak
GCT: growth cycle trough
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…Indicator approach
TCB CEI provides an optimal composite instrument for measuring and dating business/growth cycles in the aggregate economy
General economic activity is defined as a broad, multivariate concept and measured as a composite index of selected coincident (current) indicators
Measurement-oriented, market driven, no strong assumption of market equilibrium
A small set of Indicators are chosen according to a set of selection criteria: conformity (to business/growth cycles), consistent timing, economic significance, statistical adequacy, smoothness, and timeliness (currency)
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…Indicator approach
TCB LEI is used to help predict short-term cyclical fluctuations of the economy in conjunction with the CEI
Components of the LEI tend to precede and predict those of the CEI
Following the same selection criteria as CEI
The business turning points in the selected LEI indicators are identified by the Bry-Boschan algorithm
Note, in both indices, policy effect on the selected indicators exists and is endogenous, but not explicitly measured
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Summary: Indicator approach to business cycle
measurement and analysis
Recessions versus slowdowns
Monthly frequency
Use of composite indexes
Coincident index provides gauge of “current conditions”
Key to developing new indexes
Determining turning points
Leading index anticipates peaks and troughs
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TCB experience
Business cycles: definitions, theories, and indicator
approach
Methodology: applying the indicator approach
Coincident indicators
Leading indicators
Growth cycles vs. business cycles
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Measurement and analysis of business cycles with the
indicator approach has a long history
A major component of the NBER program since Burns and Mitchell
(1946)
First list of leading indicators dates back to Mitchell and Burns
(1938)
Major revisions of the lists: Moore (1950), Moore (1961), Moore
and Shiskin (1967), Zarnowitz and Boschan (1975), Hertzberg and
Beckman (1989), and The Conference Board (1996, 2012)
Turning point algorithm: Bry and Boschan (1971)
Phase Average Trend: Boschan and Ebanks (1978)
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Recall: Economic indicators and the indicator methodology
“Uniform sequences in economic activity”
Sequences are revealed in indicators classified based on timing
Coincident indicators measure current conditions.
Leading indicators move in advance of coincident indicators.
Lagging indicators move after coincident indicators.
Indicator approach based on recurrence of these relationships.
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Methodology: using the indicator approach
Turning point analysis is the main tool for selecting components:
Empirical, measurement oriented
No statistical estimation (not regression based)
Results in a taxonomy of indicators based on cyclical timing
Indicator approach is pragmatic
Does not favor any single theory over another
Well established indicator selection criteria and chronology are corner
stones
Coincident indexes help determine the business cycle turning points (esp.
when there is no NBER style business cycle dating committee)
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Ingredients: What does the indicator approach need to
construct composite indexes?
Reference chronology of peaks and troughs (e.g. in the
U.S., NBER business cycle dating committee)
Long histories of data
Seasonally adjusted data
Deflation (inflation adjustments)
Volatility adjustments
Index = Equally weighted average of month to month growth
of components
Fixed base index of level of economic activity
“Real time” performance, short-term outlook requires timely
publication
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TCB experience
Business cycles: definitions, theories, and indicator
approach
Methodology: applying the indicator approach
Coincident indicators
Growth cycles vs. business cycles
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Employees on Non-Agricultural Payrolls
Index of Industrial Production
Real Personal Income less Transfer Payments
Real Manufacturing and Trade Sales
Composition of Coincident Economic Index helps to
define the business cycle in levels of economic activity
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Basic circular flow model
Households Firms
Employment
Sales
Income
Production
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Coincident Economic Index tracks business cycles
Monthly barometer of economic conditions
Less revision than GDP
Multivariate approach
Helps to establish business cycle chronology
Target variable for leading indicators
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5 recessions
in 25 years
3 recessions in
25 years
Business cycles are asymmetric and relatively infrequent
Note: Shaded areas represent recessions as determined by the NBER Business Cycle Dating Committee.
120
100
80
60
40
20
58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12
920. The Conference Board Coincident Economic Index® (CEI) for the United States
-3
-1
-2
0
0
0
0
-1
-6
+1
+1
0
-6
-2
+1
0
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Coincident index closely tracks GDP and LEI leads both
16,000
12,000
8,000
4,000
120
80
40
60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12
Real GDP (left)
CEI (right)
-3
-1
-2
0
0
0
0
-1
-6
+1
+1
0
-6
-2
+1
0
(A
R bil. C
hain 2005 $)
Index (2004 = 100)
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TCB experience
Business cycles: definitions, theories, and indicator
approach
Methodology: applying the indicator approach
Coincident indicators
Growth cycles vs. business cycles: examples from
emerging markets indicators
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TCB China LEI and CEI
Developing coincident economic index (CEI) for China
Alternative multivariate measure of current economic conditions while considering underlying data pitfalls
5 indicators selected and combined in an index: industrial GVA, retail sales, manufacturing employment, electricity, and passenger traffic (see Guo, Ozyildirim and Zarnowitz, CEJ, 2009, and Adams, Bottelier, Ozyildirim, and Sima-Friedman, TCB mimeo, 2010)
Identifying leading indicators for China and constructing a leading economic index (LEI)
21 potential LEI indicators have been compared and assessed (see Adams, Bottelier, Ozyildirim and Sima-Friedman, TCB mimeo, 2010)
6 selected and combined in an index: Consumer Expectations Index, Total Loans Issued by Financial Institutions, 5000 Industry Enterprises Diffusion Index: Raw Materials Supply,, PMI: Manufacturing: Supplier Delivery, PMI: Manufacturing: New Export Orders, Floor Space Started: Total
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Hypotheses of downward bias in level and upward bias in
real growth in Chinese official statistics: institutional and
methodological effects (see a review by Wu, RIW, 2000)
Level measures: rural and urban housing, personal services, self consumption of output (e.g. grains), welfare benefits in kind, as well as the production of the defence industry are likely underreported (Maddison, 1998; World Bank, 1994b and 1992b; Keidel, 1992)
However, Xu (1999) maintains that as revealed by China’s third industrial census, there were severe over-reporting problems in small-sized rural enterprises
Growth rate measures: underestimated price effect (Wing, 1992 and 1994); methodological problems (Maddison, 1998; Wing 1994), especially in industrial output (Rawski, 1993; Wu, 1997, 1998, 2000, 2002, 2008; Maddison and Wu, 2008)
Political economy arguments: e.g. why the 2004 census-based overall GDP adjustment from 1992 left the 1998 growth rate untouched? (Wu, 2007)
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CEI
Industrial GVA – measurement problems
Retail sales – contain non-consumer goods in sales, and government purchases
Manufacturing employment – focusing on the urban and state sectors
Electricity – under-coverage of self consumed and self generated
Passenger traffic – substitution effect among types of transports (which do change when the economy change) cannot be captured with the current approach, and there are underreport on private-run transports
Key Indicators Used
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LEI
Real total loans – ignoring activities supported by informal finance
Consumer expectation index – sufficient strata sampling?
Survey on 5000 industrial enterprises – biases towards manufacturing, L-M sized and state firms?
Real export value – policy effect or induced misreporting (for rebates) and misinvoicing (exchange rate policy related)
PMI subindexes - biases towards manufacturing, L-M sized and state firms?
Are all deflators appropriate or sufficient?
Key Indicators Used
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200
100
80
60
50
40
30
20
10
400
200
160
120100
80
60
40
20
86 88 90 92 94 96 98 00 02 04 06 08 10
The Conference Board Leading Economic Index® (LEI) for China, LHS
The Conference Board Coincident Economic Index® (CEI) for China, RHS
Peak: 1988:07Trough: 1989:10
-6
-11
Ind
ex, 2
00
4=
10
0
LEI and CEI for China (2004 = 100)
Source: The Conference Board
downturn
slowdown
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Sources: The Conference Board, NBS
A Closer Look at CEI for China
15
14
13
12
11
10
9
8
7
6
52000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
The Conference Board Coincident Economic Index® (CEI) for China
China GDP
year over year percent change
Q 3, 2011
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Trend of China CEI
Source: The Conference Board
200180
160
140
120
100
80
60
40
2086 88 90 92 94 96 98 00 02 04 06 08 10
China CEI
Trend of China CEI7
/88
10
/89
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Removal of CEI’s growth produces chronology of growth
cycles – fluctuations around the growth trend
-8
-6
-4
-2
0
2
4
6
8
10
86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10
Modified CEI, deviations from trend
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Source: The Conference Board
LEI for China has a median lead of 6-8 months ahead of
CEI growth cycles
-16
-12
-8
-4
0
4
8
12
86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10
China CEI, deviations from trend
China LEI, deviations from trend
88:02 90:02 93:02 93:11 95:09 98:02 00:01 02:02 03:12 04:07 08:03 08:12
-1 -12
-21 -2 -4 -11
-8
-5
-39
-17 -1 -5
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Source: TCB
For India: combining monthly CEI and quarterly GDP to
obtain a business cycle chronology
200
160
120
80
4090 92 94 96 98 00 02 04 06 08 10 12
India CEI+GDP, 2004=100
08/09
09/01
Industrial Production: General Index
Electricity Generation
Automobile Sales: Passenger Vehicle
Total Imports
GDP
Note: shaded areas represent chronologies derived from India CEI+GDP determined by Bry-Boschen algorithm
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Source: TCB
India experimental CEI coincides at both peak and
trough of the single recession
200
160
120
80
4090 92 94 96 98 00 02 04 06 08 10 12
India CEI, 2004=100
08/09
09/01
Industrial Production: General Index
Electricity Generation
Automobile Sales: Passenger Vehicle
Total Imports
0
0
Note: shaded areas represent chronologies derived from India CEI+GDP determined by Bry-Boschen algorithm
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Source: TCB
India CEI+ Non-agricultural GDP makes no difference in
turning points
200
160
120
80
4090 92 94 96 98 00 02 04 06 08 10 12
India CEI+GDP
India CEI+ Non agro GDP
India CEI + GDP
08/09
09/01
Note: shaded areas represent chronologies derived from India CEI+GDP determined by Bry-Boschen
algorithm
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Source: TCB
India CEI+GDP growth trend estimated using Hodrick-
Prescott filter
Note: shaded areas represent chronologies derived from India CEI+GDP determined by Bry-Boschan algorithm
200
160
120
80
4090 92 94 96 98 00 02 04 06 08 10 12
India CEI + GDPGrowth trend of India CEI+GDP
08/09
09/01
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Source: TCB
India growth cycles – deviations from the estimated growth
trend
-8
-6
-4
-2
0
2
4
6
8
90 92 94 96 98 00 02 04 06 08 10 12
India CEI deviations from trendIndia CEI+GDP deviations from trend
08/0
4
09/0
2
India growth cycles
91/0
3
94/0
5
96/0
2
97/0
8
00/0
2
02/1
2
03/1
2
04/0
5
05/0
5
05/1
1
11/0
2
Note: shaded areas represent chronologies derived from deviations from the trend of India CEI+GDP determined by Bry-Boschan algorithm
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Growth cycles in India and China look increasingly
synchronized
Source: TCB
Peak Trough Peak Trough
Feb-1988 Oct-1989
Mar-1991 May-1995 Feb-1993 Nov-1993
Feb-1996 Aug-1997 Sep-1995 May-1998
Feb-2000 Dec-2002 Jan-2000 Feb-2002
Dec-2003 May-2004 Dec-2003 Jun-2004
May-2005 Nov-2005
Apr-2008 Feb-2009 Mar-2008 Jan-2009
Feb-2011 Aug-2010 Feb-2011
India CEI+GDP China CEI
Growth Cyles Growth Cyles
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Developing a CEI for Brazil
Source: FGV/TCB
Series Description Type Range Adjustments Source
yt industrial production Index Jan/1991 - Dec/2012 seasonally adjusted IBGE
et electric energy consumption GWh Jan/1980 - Dec/2012 seasonally adjusted Eletrobras
cpt corrugated paper
expedition Tonnes Jan/1980 - Dec/2012 seasonally adjusted ABPO
st volume of sales in the retail
market Index Jan/2003 - Dec/2012 seasonally adjusted IBGE
nt_om occupied population - old
methodology
Number of people
(thousands) May/1982 - Dec/2002 seasonally adjusted IBGE
nt_om occupied population Number of
people (thousands)
Mar/2002 - Dec/2012 seasonally adjusted IBGE
it_om average real income - old
methodology R$ Jul/1994 - Nov/2002
deflated series* seasonally adjusted
IBGE
it average real income R$ Feb/2002 - Dec/2012 seasonally adjusted IBGE
* Using the General Price Index (IGP-DI) - prices of Jan/2012
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Experimental CEI for Brazil
Source: FGV/TCB
*The shaded areas in the graph correspond to recession periods dated by the Bry-Boschan algorithm.
Period: Jan/1996 - Dec/2012
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Experimental CEI for Brazil
Source: FGV/TCB
*The shaded areas in the graph correspond to recession periods dated by the Bry-Boschan algorithm.
Period: Jan/1996 - Dec/2012
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Business cycle chronology for Brazil
Source: FGV/TCB
CODACE Chronology CEI - without
overlapping series
CEI - overlapping series
Peaks Troughs Peaks Troughs Peaks Troughs
Oct-80 Feb-83 - - - - - - - -
Feb-87 Oct-88 - - - - - - - -
Jun-89 Dec-91 - - - - - - - -
Dec-94 Sep-95 - - - - - - - -
Oct-97 Feb-99 Oct-97 Feb-99 0 0 Oct-97 Feb-99 0 0
Dec-00 Sep-01 Dec-00 Oct-01 0 +1 Dec-00 Sep-01 0 0
Oct-02 Jun-03 - - Aug-02 Jun-03 -2 0
Jul-08 Jan-09 Sep-08 Jan-09 +2 0 Jul-08 Dec-08 0 -1
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Concluding Remarks
Despite potential biases and problems we may still argue that the TCB leading and coincident indices are among the best tools to measure and monitor the short term cyclical performance for emerging market economies
They can provide (relatively) independent benchmarks as measures of cyclical developments
One type of comparison is kind of a reality check with economic agent’s perceptions on whether times were good or bad – was economic activity expanding or contracting in general
We can look at the deviations from trend in the CEI to get a chronology for the growth cycle for greater understanding of periods of above and below trend growth
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Appendix
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Summary of basic steps in applying indicator approach
Identify potential indicators
Target or reference series
Potential coincident and leading indicators
Analyze indicators
Characterize indicators as coincident, leading or lagging
Grouping
Index Construction
Bring cycles into focus
Index improves on the performance of the individual indicators
Evaluate and test index
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Summary: identifying potential indicators
Criteria for Selection
Sources
Government statistics - national, industry and regional level
Firm data
Trade groups (e.g. Mortgage Bankers Association, National
Association of Realtors)
Financial indicators (stock and commodity prices)
Others (e.g., The Conference Board, credit rating agencies, IMF,
specialized researchers)
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Determine if indicators are leading, coincident or lagging
Examine correlations between indicators
Determine turning points - Bry-Boschan algorithm*
Minimum of 5 months for a phase
Minimum of15 months for a cycle
For flat or double turning point, select the last turning point
Extreme values ignored
Categorizing and grouping
Data analysis to transform data to its most useful form.
Seasonal adjustment, deflation, moving average, interpolation
*See Bry, G. and Boschan, C.: 1971, Cyclical Analysis of Time Series: Selected Procedures and Computer
Programs, NBER.
Summary: analyzing indicators
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Index should improve on the performance of the individual indicators.
Eliminate or reduce missed or extra cycles.
Result in leads or lags in more useful leads.
Mechanics
Volatility adjustments – use factors that are inversely related to the standard deviation of monthly changes in component
Equal weights
Trend adjustment
Interpolation, moving average
Summary: index construction
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1. Calculate month-to-month changes.
If the component X is in percent change form or an interest rate, simple arithmetic differences are calculated: x t=X t - X t-1. If not, a symmetric percent change is used: x t = 200 * (X t - X t-1)/(X t + X t-1).
2. Adjust month-to-month change by multiplying them with the component’s standardization factor (the inverse of the standard deviation of monthly changes in component). The result is the monthly contribution of each component.
3. Add the adjusted monthly contribution of each component (from Step 2). This results in the sum of adjusted contributions, which is the monthly growth rate of index (it).
Steps for Index Construction
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4. The sum of the adjusted contributions (from Step 3) of the leading index is adjusted to equate its trend to that of the composite index. This is accomplished by adding an adjustment factor, a, to the growth rates of the index each month (it`= it+ a).
The trend adjustment factor for the leading index is computed by subtracting its average monthly growth rate from the average monthly growth rate of the composite index.
5. Compute preliminary levels of the index using the symmetric percent change formula.
The first month's value is I1=100. The second month's value I2 = I1 * (200+i2`)/(200-i2`) and this formula is used recursively to compute the index levels for each month that data are available.
6. Rebasing (e.g. 2004 = 100). The history is multiplied by 100 and divided by the average for the twelve months in the base year.
Steps for Index Construction…(cont.)
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Selection Criteria for Components
Economic Significance — cyclical timing must have economic
meaning and logical.
Conformity — the series must conform well to the business cycle.
Consistent Timing — the series must exhibit a consistent timing
pattern as a leading, coincident or lagging indicator.
Smoothness — month-to-month movements must not be too
erratic.
Statistical Adequacy — data must be collected and processed in a
statistically reliable way.
Currency or Timeliness— series must be published on a
reasonably prompt schedule, preferably within a month.