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Challenges for estimating and forecasting Challenges for estimating and forecasting macroeconomic trends during financial crises: macroeconomic trends during financial crises:

implications for counter-cyclical policies implications for counter-cyclical policies

Pingfan HongPingfan HongChief for Global Economic MonitoringChief for Global Economic Monitoring

UN/DESAUN/DESA

International Seminar at Ottawa, Canada International Seminar at Ottawa, Canada 27-29 May 200927-29 May 2009

Views expressed here are solely those of the speaker and they do not necessarily represent those Views expressed here are solely those of the speaker and they do not necessarily represent those of the United Nationsof the United Nations

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OutlineOutline

Introduction Introduction Forecasting performance of UN/LINK Forecasting performance of UN/LINK

global modeling system global modeling system High Frequency Modeling for Rolling High Frequency Modeling for Rolling

estimation and forecast estimation and forecast ““turning point”: Over-year-ago (oya) turning point”: Over-year-ago (oya)

Quarterly GDP growth versus Seasonally Quarterly GDP growth versus Seasonally Adjusted Annual Rate (SAAR) of Quarterly Adjusted Annual Rate (SAAR) of Quarterly GDP growthGDP growth

The importance of correctly estimating The importance of correctly estimating potential output potential output

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Introduction Introduction

Estimating versus forecasting Estimating versus forecasting

Estimating:Estimating:

Forecasting:Forecasting: Importance of estimating and forecasting for Importance of estimating and forecasting for

counter-cyclical macroeconomic policy: counter-cyclical macroeconomic policy: timeliness, consistent, accuracy, “turning timeliness, consistent, accuracy, “turning point”, and correct estimate of the potential gap point”, and correct estimate of the potential gap

)/( 1 ttft IyEy

)/( ttet IyEy

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Forecasting performance of Forecasting performance of UN/LINK global modelingUN/LINK global modeling (1) (1)

Figure 1. Forecasting world GDP

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Forecasting performance of Forecasting performance of UN/LINK global modelingUN/LINK global modeling (2) (2)

Figure 2. forecasting GDP for developed countries

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Forecasting performance of Forecasting performance of UN/LINK global modelingUN/LINK global modeling (3) (3)

figure 3. forecasting GDP for developing countries

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Forecasting performance of Forecasting performance of UN/LINK global modelingUN/LINK global modeling (4) (4)

world developed economies

developing countries

Mean 0.02 0.04 -0.36

Median 0.05 0.05 -0.1

Standard Deviation 0.7 0.76 1.25

Fraction of positive errors 0.52 0.5 0.42

Serial correlation -0.2 -0.1 0.29

Source: DESA

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High Frequency Modeling for High Frequency Modeling for rolling estimating quarterly GDProlling estimating quarterly GDP

Collecting weekly data stream Collecting weekly data stream Principle Component Principle Component ARIMAARIMA Weekly rolling estimate and forecast of Weekly rolling estimate and forecast of

quarterly GDPquarterly GDP

Sources for slides 8-12:Sources for slides 8-12: L.R. Klein and W. Mak, University of L.R. Klein and W. Mak, University of Pennsylvania Current Quarter Model of the United States Economy Pennsylvania Current Quarter Model of the United States Economy

Y. Inada, Konan University Current Quarter Model Forecast Y. Inada, Konan University Current Quarter Model Forecast For the Japanese EconomyFor the Japanese Economy

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Example: US weekly data stream Example: US weekly data stream Date Economic Indicator for Latest and Prior Month Apr 01 Construction Spending February -0.9% -3.5% Apr 01 Auto Sales March 9.9 Million 9.1 Million Apr 02 Manuf Ships, Inv, & Orders February -0.1%, -1.2%, 1.8% -

2.6%, -1.1%, -3.5% Apr 03 Nonfarm Payroll Employment March -663,000 -651,000 Apr 07 Consumer Credit Outstanding February -$7.5 billion $8.1

billion Apr 09 Export/Import Price Index March -0.6%, 0.5% -0.3%, -0.1% Apr 09 Trade Balance February -$26.0 billion -$36.2 billion Apr 15 Producer Price Index, Total & Core March -1.2%, 0.0% 0.1%,

0.2% Apr 14 Retail Sales, Total & Ex-Auto March -1.1%, 0.9% 0.3%, 1.0% Apr 15 Industrial Production March -1.5% -1.5% Apr 14 Business Inventories February -1.3% -1.3% Apr 15 Consumer Price Index, Total & Core March -0.1%, 0.2%

0.4%, 0.2% Apr 16 Housing Starts February 510,000 572,000

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Example: indicators used in US Example: indicators used in US model for estimating quarterly model for estimating quarterly

GDP GDP Industrial Production Index Manufacturers’ orders, deflated by producer price index Manufacturers’ shipments, deflated by producer price index Manufacturers’ unfilled orders, deflated by producer price index Yield spread between 6-month commercial paper and 6-month

treasury bills Real interest rate (6-month commercial paper yield adjusted by

consumer price index) Real M1, adjusted by consumer price index Real retail sales, adjusted by consumer price index Real personal income, adjusted by consumer price index Real 10-year treasury yield Yield spread between 10- and 1-year treasury bills Nonfarm payrolls Average weekly hours, production workers: total private Trade-weighted value of the US dollar, nominal broad dollar

index

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Example: Equations for GDP and Example: Equations for GDP and PGDP in US model PGDP in US model

Dlog (QGDP) = 0684 – 0.954 Dlog C1 + 0.304 Dlog C2

-0.0661 Dlog C6 – 0.295 Dlog C7

+ 0.581 AR(1) – 0.677 MA(1)

Dlog (QPGDP) = 0.817 – 2.463 Dlog C1 + 0.925 Dlog C2

+ 1.383 Dlog C3 – 5.113 Dlog C4 + 4.189 Dlog C5 – 2.233 Dlog C6 + 0.908 MA(4)

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Example: Japan H-F model forecast Example: Japan H-F model forecast versus consensus forecastversus consensus forecast

Source: Y. InadaSource: Y. Inada

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Convergence in the rolling forecast of the US H-F Convergence in the rolling forecast of the US H-F modelmodel

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2008q1 q2 q3 q4 2009q1 offical

est m1

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M1 M2 M3 M4

Mean error -2.625 -0.5475 -0.665 -0.8375

RMSE 3.607652 1.853126 1.144312 1.4058

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Convergence in the rolling forecast of the Japan H-F Convergence in the rolling forecast of the Japan H-F modelmodel

Rolling estimate of GDP for Japan

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est m1

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Mean error -7.3 -6.7 -3.2 -0.7

RMSE 8.4 7.2 4.5 2.1

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““turning point”: oya versus saarturning point”: oya versus saarExample of China’s GDPExample of China’s GDP

Sources: China NBS, JPMSources: China NBS, JPM

China GDP Growth: oya vs saar

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Importance of correct estimate of potential Importance of correct estimate of potential output for counter cyclical macroeconomic output for counter cyclical macroeconomic

policypolicy

Taylor rule:Taylor rule:

Hodrick-Prescott filter for estimating potential GDP growth :Hodrick-Prescott filter for estimating potential GDP growth :

Production function for estimating potential GDP growth: Production function for estimating potential GDP growth:

))(1()( *** yyri tttt

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1

21

1

2 )]()[()(min

tt

T

ttt

T

ttty

),( *** lkfy

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Estimate of output Gap for the US Estimate of output Gap for the US economyeconomy

by H-P filter by H-P filter US GDP GAp by H-P filter

10800

10900

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USA_YGDP

USA_YGDP_HP2005

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Estimate output Gap for the US Estimate output Gap for the US economyeconomy

by production function by production function

Source: Business WeekSource: Business Week

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111009-8

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1970 1975 1980 1985 1990 1995 2000 05 10

High-income

Developing

Are these Are these Output GAPOutput GAPs corrected s corrected estimated?estimated?

Output gap % of GDPRecord levels of spare capacity

Source: World Bank.

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Concluding remarksConcluding remarks

It’s a big challenge to make a timely It’s a big challenge to make a timely and consistent estimate and forecast and consistent estimate and forecast for economic trends during financial for economic trends during financial crisiscrisis

But they are crucial for But they are crucial for macroeconomic policiesmacroeconomic policies

We can make improvement We can make improvement