On Trends, Cycles, and Turning Points of Emerging Economies: Examples …epge.fgv.br/conferencias/business-cycles/files/ataman... · 2019-01-10 · monetary growth rates followed

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

http://www.conference-board.org/


About The Conference Board



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



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



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



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



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



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



-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



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)



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



TCB experience


approach



Leading indicators

Growth cycles vs. business cycles



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.



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



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



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



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



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



…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)



…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



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



TCB experience


approach



Leading indicators




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)



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.



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)



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



TCB experience


approach






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



Basic circular flow model

Households Firms

Employment

Sales

Income

Production



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



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



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)



TCB experience


approach



Growth cycles vs. business cycles: examples from

emerging markets indicators



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



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)



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



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



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)


downturn

slowdown



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



Trend of China CEI


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



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




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



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



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



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



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



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



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



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



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



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



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



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



Appendix



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



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)



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



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



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



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.)



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.


Documents

On Trends, Cycles, and Turning Points of Emerging Economies: Examples …epge.fgv.br/conferencias/business-cycles/files/ataman... · 2019-01-10 · monetary growth rates followed