On trends, cycles, and turning points of emerging economies: examples from China, India, Brazil

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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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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

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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

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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

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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

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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

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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

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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 1982

Money created by bank lending to business; short-term financing of long-short-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 1977

General 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 Processes Responsive Originating Are Cycles Linked to Growth? Main Factors

Special Features Authors & 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

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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)

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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 Lev el of ec onom ic ac tiv it y D ev iat ion from long term t rend

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

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TCB experience

Business cycles: definitions, theories, and indicator

approach

Methodology: applying the indicator approach

Coincident indicators

Leading indicators

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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)

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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.

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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

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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

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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

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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 b i l . C h a i n 2 0 0 5 $ ) I n d e x ( 2 0 0 4 = 1 0 0 )

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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

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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

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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?

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38 200 100 80 60 50 40 30 20 10 400 200 160 120 100 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: 1988:07 Trough: 1989:10 -6 -11 In d e x , 2 0 0 4 = 1 0 0

LEI and CEI for China (2004 = 100)

Source: The Conference Board

downturn

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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 5 2000 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

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Trend of China CEI

200 180 160 140 120 100 80 60 40 20 86 88 90 92 94 96 98 00 02 04 06 08 10 China CEI

Trend of China CEI

7 /8 8 1 0 /8 9

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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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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 40 90 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

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Source: TCB

India experimental CEI coincides at both peak and

trough of the single recession

200 160 120 80 40 90 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

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Source: TCB

India CEI+ Non-agricultural GDP makes no difference in

turning points

200 160 120 80 40 90 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 40 90 92 94 96 98 00 02 04 06 08 10 12 India CEI + GDP

Growth 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 trend

India CEI+GDP deviations from trend

0 8 /0 4 0 9 /0 2

India growth cycles

9 1 /0 3 9 4 /0 5 9 6 /0 2 9 7 /0 8 0 0 /0 2 0 2 /1 2 0 3 /1 2 0 4 /0 5 0 5 /0 5 0 5 /1 1 1 1 /0 2

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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

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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

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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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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

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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.

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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

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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 (i_t).

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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 (i_t`= i_t+ 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.

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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