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Economic Projection at NESDB

International Seminar on Timeliness Methodology and Comparability of Rapid Estimates of Economic Trends

Ottawa, Canada27-29 May 2009

Wichayayuth BoonchitEconomic Modeling & Projection Division

Macroeconomic OfficeOffice of the National Economic & Social Development Board

Thailandwichayayuth@nesdb.go.th

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NESDB’s Functions and Responsibilities

1. To study and analyze the national economic and social condition for development planning, and recommend related policy issues to the government

2. To follow-up the performance of the National Plan and monitor and evaluate some major development programs and projects

3. To coordinate with all agencies concerned in implementation of the development plan

4. To appraise and evaluate development programs/projects of public agencies

5. To undertake any assignments by the government

Economic projections

Impact analysis

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NESDB and Economic Projections

Long-term projection Long-term projection

Medium-term projection

(guideline for formulating development plan &

development strategy)

Medium-term projection

(guideline for formulating development plan &

development strategy)

Potential GDP

(subjected to strong assumptions)

Potential GDP

(subjected to strong assumptions)

Medium Term Macroeconomic Frameworks (Trends and targets)

Medium Term Macroeconomic Frameworks (Trends and targets)

Medium Term Expenditure Framework: MTEF

Medium Term Expenditure Framework: MTEF

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The Quarterly Economic Report

Contents

• Current economic conditions

• Outlook for remaining of the year

• Yearly GDP projection

(Quarterly GDP forecast is part of yearly GDP projection)

• Policy guidelines

Release to the public (release on the same date with actual QGDP)

Submit to cabinet for consideration

Contents

• Current economic conditions

• Outlook for remaining of the year

• Yearly GDP projection

(Quarterly GDP forecast is part of yearly GDP projection)

• Policy guidelines

Release to the public (release on the same date with actual QGDP)

Submit to cabinet for consideration

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Annual

GDP forecast

Annual

GDP forecast

o Forecast economic growth (focuses mainly on expenditure side) o Current account balance o Inflation

o Forecast economic growth (focuses mainly on expenditure side) o Current account balance o Inflation

o Projection in the range of 1% in February and May

o Projection range will be reduced to 0.5% in August

o Point estimate in November (the projection for following year will be also released)

o Projection in the range of 1% in February and May

o Projection range will be reduced to 0.5% in August

o Point estimate in November (the projection for following year will be also released)

o Update on quarterly basis (Assumptions & databases, revise if necessary) o Requires quarterly forecast

o Update on quarterly basis (Assumptions & databases, revise if necessary) o Requires quarterly forecast

NESDB and Economic Projections

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Tools for Quarterly and Yearly Economic Projection

- Current Quarter Model: CQMAn old style macro-econometric model in the tradition of Lawrence Klein that are heavily

reliance on econometric estimation (Other models of this types are Project Link, DRI, Wharton model) - Quarterly Financial Model: QFM

The newer style macro econometric models with greater reliance on economic theory.

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Adjust Annual forecast

(CGE + Fin. Programming)

CQM’s forecast(2 quarters)

Current Quarter Model CQM

High frequency data (Monthly)

Beginning

comparison

Assumptions &exogenous variables

(Quarterly)

Quarterly Financial Model(QFM)

QFM’s forecast(4 quarters)

Annual forecast is the mixtures of CQM and QFM forecastAnnual forecast is the mixtures of CQM and QFM forecast

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Yearly GDP Projection

Release date Actual CQM forecast

QFM forecast

23rd February Q4 of previous year Q1 Q2-Q4

March-April Q4 of previous year Q1_Q2 Q3-Q4

25th May Q1 Q2 Q3-Q4

Jun-July Q1 Q2-Q3 Q4

August Q2 Q3 Q4

September-July Q2 Q3-Q4 -

November Q3 Q4 -

December-January Q3 Q4 – Q1 (next year) -

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CQM is first developed at the University of Pennsylvania by Noble Laureate Lawrence R. Klein

Concept

• Utilize high but different frequencies information (indicator variables) to estimate immediate future values of GDP both on demand and supply sides

• The estimates are made on the basis of bridge equations that link high frequency data (indicator variables) to low frequency data (NIPA).

• The procedure is first to predict the future value of high frequency data (indicator variables) by using time-series analysis (ARMA process) and then estimate future values of NIPA by using bridge equations.

• The estimate values of GDP will be updated on the rolling basis, when new piece of information or new figure for one of indicator variable become available (not late than 15th of each month).

CQM is first developed at the University of Pennsylvania by Noble Laureate Lawrence R. Klein

Concept

• Utilize high but different frequencies information (indicator variables) to estimate immediate future values of GDP both on demand and supply sides

• The estimates are made on the basis of bridge equations that link high frequency data (indicator variables) to low frequency data (NIPA).

• The procedure is first to predict the future value of high frequency data (indicator variables) by using time-series analysis (ARMA process) and then estimate future values of NIPA by using bridge equations.

• The estimate values of GDP will be updated on the rolling basis, when new piece of information or new figure for one of indicator variable become available (not late than 15th of each month).

Current Quarter Model: CQMTool for rapid estimation

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o Projected NIPA data (National

income and product account data)

o Projected NIPA data (National

income and product account data)

o NIPA data (National income and product Account data)

o NIPA data (National income and product Account data)

o Indicator variables ( Monthly) with projected periodo Indicator variables ( Monthly) with projected period

o Indicator variables (Daily,

Weekly , and Monthly)

o Indicator variables (Daily,

Weekly , and Monthly)

“bridge”equations

estimate

ARMA and ARIMA techniques

Econometric techniques

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Jan.

Feb.

Mar.

Apr. May.

Jun. Jul. Aug.

Sep. Oct. Nov.

Dec. Jan.

Feb.

Mar.

Q4 of previous year

Q1 Q2 Q3 Q4

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How to Construct CQM?

• Search for suitable high frequencies information (indicator variables)

(availability on timely basis, reliability or variability)

• Plot time series that are selected to obtain broad ideas of trend, cyclical variability and seasonality of variable.

• Test for stationarity by applying Augmented-Dickey Fuller Test and Philips Perron Test

• Select appropriate ARMA Term (p,d,q) for each indicator variable

• Estimate Bridge equations that link high frequencies information (indicator variables) to low frequencies variables (NIPA)

• Search for suitable high frequencies information (indicator variables)

(availability on timely basis, reliability or variability)

• Plot time series that are selected to obtain broad ideas of trend, cyclical variability and seasonality of variable.

• Test for stationarity by applying Augmented-Dickey Fuller Test and Philips Perron Test

• Select appropriate ARMA Term (p,d,q) for each indicator variable

• Estimate Bridge equations that link high frequencies information (indicator variables) to low frequencies variables (NIPA)

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Example: relationship between indicator variables and NIPA variables

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

1,100,000

1993Q1

1994Q1

1995Q1

1996Q1

1997Q1

1998Q1

1999Q1

2000Q1

2001Q1

2002Q1

2003Q1

2004Q1

2005Q1

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

110,000

PCE (LHS) value add tax(RHS)

PCE at current prices and Value added tax

120

130

140

150

160

170

180

190

200

1993Q1

1994Q1

1995Q1

1996Q1

1997Q1

1998Q1

1999Q1

2000Q1

2001Q1

2002Q1

2003Q1

2004Q1

2005Q1

60

70

80

90

100

110

120

PCE price deflator (LHS) CPI (RHS)

PCE Prices deflator and CPI

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Manufacturing at 1988 prices and MPI Deflator of Manufacturing and PPI Manufac.

150,000

200,000

250,000

300,000

350,000

400,000

450,000

1993Q1

1994Q1

1995Q1

1996Q1

1997Q1

1998Q1

1999Q1

2000Q1

2001Q1

2002Q1

2003Q1

2004Q1

2005Q1

40

60

80

100

120

140

160

180

Manufacturing at 1988 price (LHS) MPI (RHS)

110

120

130

140

150

160

170

1993Q1

1994Q1

1995Q1

1996Q1

1997Q1

1998Q1

1999Q1

2000Q1

2001Q1

2002Q1

2003Q1

2004Q1

2005Q1

60

70

80

90

100

110

120

130

140

deflator of MPI PPI (RHS)

Example: relationship between indicator variables and NIPA variables

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Hotel and Restaurant at current market prices and VAT

Deflator of Hotel and Restaurant and CPI Recreation

0

5,000,000,000

10,000,000,000

15,000,000,000

20,000,000,000

25,000,000,000

30,000,000,000

35,000,000,000

40,000,000,000

45,000,000,000

1993

Q1

1994

Q1

1995

Q1

1996

Q1

1997

Q1

1998

Q1

1999

Q1

2000

Q1

2001

Q1

2002

Q1

2003

Q1

2004

Q1

2005

Q1

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

VAT (RHS) Hotel and Restaurant at current prices (LHS)

150

170

190

210

230

250

270

1993Q1

1994Q1

1995Q1

1996Q1

1997Q1

1998Q1

1999Q1

2000Q1

2001Q1

2002Q1

2003Q1

2004Q1

2005Q1

70

75

80

85

90

95

100

105

Deflator of Hotel and RestaurantCPI recreation (RHS)

Example: relationship between indicator variables and NIPA variables

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A A A A P P P P P

Jan.

Monthly indicators(Indicator variables)

Q1XA Q2XP Q3XP

A E E

Bridge EquationQNIPA = a +bQXNIPA

A= Actual valueP= Projected valueE=Estimated value

1. Forecast indicator variables by using ARMA equation

2. Transform from monthly indicators to quarterly indicators

3. Use bridge equations to estimate NIPA variables

On June 15: Monthly indicators as of April 31 become available

Feb.

Mar.

Apr. May.

Jun. Jul. Aug.

Sep.

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NIPA & Indicator Variables

(A) Expenditure Side

NIPA Monthly Indicators

Nominal/Real Expenditures

Price Deflator

Private Consumption Expenditure

Retail Sales IndexSales of Passenger Car

CPI

Government Expenditure Exogenous Exogenous

Gross Fixed Capital Formation

Identity Identity

- Construction • Construction area permitted (lag terms)

• Cement consumption

Construction price index

- Machinery and Equipment

• Commercial car sales• Import volume index of

capital goods

PPI of capital equipment

Exports of Goods Export of goods (BOP) Unit value of exports

Exports of Services Receipts of services income and transfer (BOP)

Weighted avg. of CPIs

Imports of Goods Imports of goods (BOP) Unit value of imports

Imports of Services Payments of services income and transfer (BOP)

Nominal imports of goods

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(B) Production Side

NIPA Monthly Indicators

Nominal/Real Expenditures Price Deflator

Agriculture, Hunting and Forestry

Crop production index Crop price index

Fishing Export Volume of Fishery PPI Fishery product

Mining & Quarrying Production index of natural gas

PPI Mining

Manufacturing Manufacturing production index

PPI Manufacturing

Electricity, Gas & Water Supply

Electricity consumption CPI Electricity, fuel and water

Construction • Cement consumption• Construction area

permitted

Construction price index

Wholesale and Retail Trade VAT for this sector CPI

Hotel and Restaurant VAT for this sector CPI Recreation

Transportation and Communication

VAT for this sector CPI Transportation

Financial Interest rate spread CPI

Real Estate & Business activity

VAT for this sector CPI Shelter

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Example: CQM Forecast for Q1/2009

2008 2009 Q1

Q1 Q2 Q3 Q4 Q1F Q2F Q1F** Q2F Actual

Private Consumption 2.7 2.5 2.7 2.2 -1.3 -2.1 -2.1 -0.5 -2.6

Government Consumption -0.4 -3.7 -2.9 10.4 7.5 2.0 11.2 9.5 2.8

Gross Fixed Capital Formation

5.4 1.9 0.6 -3.3 -7.8 -4.3 -13.7 -9.7 -15.8

- Private 6.5 4.3 3.5 -1.3 -7.6 -4.2 -14.0 -12.0 -17.7

- Public 1.9 -5.2 -5.5 -10.2 -8.5 -4.5 -12.7 -2.3 -9.1

Export of Goods and Services

8.9 11.9 11.2 -8.6 -16.1 -18.6 -17.1 -21.1 -16.4

- Export of Goods 8.3 13.2 12.6 -8.9 -19.7 -22.4 -17.8 -20.9 -17.9

- Export of Services 11.1 5.6 4.9 -7.5 -2.4 -0.1 -14.6 -21.9 -11.0

Import of Goods and Services

9.3 6.7 13.1 1.0 -19.4 -22.6 -23.6 -28.7 -31.4

- Import of Goods 10.0 5.2 12.5 0.1 -23.7 -27.0 -29.3 -34.7 -36.1

- Import of Services 6.9 13.7 16.2 4.5 -2.0 -3.1 -0.5 -2.6 -12.3

Gross Domestic Expenditure

6.2 5.5 3.8 -4.3 -7.2 -8.2 -6.5 -5.8 -7.1

Agricultural Sector 3.1 8.6 9.6 1.8 1.6 2.7 0.8 1.3 3.5

Non-Agricultural Sector 6.2 5.0 3.5 -5.0 -8.0 -6.7 -7.2 -6.4 -8.1

Manufacturing Sector 9.5 7.7 6.1 -6.8 -15.9 -14.9 -13.6 -12.7 -14.9

Construction 1.1 -3.4 -4.5 -12.8 -10.5 -5.2 -9.7 -12.3 -7.9

Wholesale and retail trade 4.1 3.4 3.1 -3.0 -1.9 -2.2 -0.6 -1.3 -4.0

Gross Domestic Product 6.0 5.3 3.9 -4.3 -7.2 -6.0 -6.5 -5.8 -7.1

* With 1 month actual indicator variables, ** With 3 months actual indicator variables

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Quarterly Financial Model: QFM

• QFM can be classified as a newer style macro econometric models.

• QFM comprise of 29 endogenous aggregate variables and 28 exogenous aggregate variables

• QFM forecast is used to reconcile with CQM forecast and to form annual projection

• QFM is also used for analyzing shocks in financial sector

• QFM can be classified as a newer style macro econometric models.

• QFM comprise of 29 endogenous aggregate variables and 28 exogenous aggregate variables

• QFM forecast is used to reconcile with CQM forecast and to form annual projection

• QFM is also used for analyzing shocks in financial sector

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List of Endogenous Variables

• Ctp = Private consumption

expenditures• CAD = Current account deficit

• CFt = Net capital inflows • EX = Aggregate exports

• GRt = Total government revenue

• GSt = Government surplus

• Itp = Private investment

• IMi = Import classified by commodity groups,

(where i=1,2,3,…,10)• IM = Aggregate imports• Ms = Money Supply

• MB = Money base

• NFA = Net foreign asset

• NES = Net exports of services

• PXDi = Relative price of export to domestic price index (classified by commodity groups), where i=1,2,3,…,10

• Ptd = Consumer price index• PIMDi = Relative price of import to

domestic price index (classified by commodity groups), where i=1,2,3,…,10

• rd = Domestic interest rate (MLR)• St = Saving• TAXt = Government tax revenue• Xi = Exports classified by commodity

groups, • where i=1,2,3,…,10• VAT = Value added and business tax

revenue• Yt = Gross domestic product• YA = GDP from agriculture sector• YC = GDP from• YE = GDP from electricity and water

supply• YM = GDP from industrial sector• Yother = GDP from other sectors• YD = Disposable income• YD_er = Disposable income in USD

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List of Exogenous Variables

• Ctg = Public consumption expenditures• CONP = Claims on nonfinancial public

enterprise (collected since January 1995)

• CREDIT = Export credit (USD)• DISt = Statistical discrepancies• et =Bilateral exchange rate (baht/USD)• EOS = errors and omission portions in

balance of payments• Get = total government expenditure• GRWTHUS = Growth of US GDP• Itg = Public capital formation

expenditures real • NCOG = Net claims on Government by

bank of Thailand• Pid = Domestic price index classified

by commodity groups, where i=1,2,3,…,9

• PtE = Price expectation in period t (adaptive)

• Ptf = Foreign price index (USCPI)• PtIM = Import price index• PtX = Export price index• PtYA = Price index of agriculture sector

• PtYC = Price index of construction• PtYE = Price of electricity and

water supply• PtYA = Price index of industrial

sector• PIMi = Import price index

classified by commodity groups, where i=1,2,3,…,10

• PXi = Export price index classified by commodity groups, where i=1,2,3,…,10

• ri = Interbank rate• rf = Foreign interest rate (LIBOR)• Qi = Time trend of export i

classified by commodity groups, where i=1,2,3,…,10

• Qie = Expected export i (time trend of export i classified by commodity groups classified), where i=1,2,3,…,10

• Qt = Output capacity (time trend of aggregate exports)

• Ytf = world gross domestic products (USGDP)

• Yte = Expected output (time trend of Yt)

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Recursive Dynamic Applied General Equilibrium Model Exogenous saving & exogenous growth Used for analyzing the impacts of some certain shocks

and policy changes as well as long-term potential GDP projection

Ramsey-Cass-Koopmans Dynamic General Equilibrium Model Perfect foresight dynamic CGE model Endogenous saving but exogenous growth

Mostly used for analyzing of economic shocks, i.e. oil shock and agricultural TFP shock

Tools for Impact Analysis and Medium & Long-term Projections

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Recursive Dynamic AGE model

Based on exogenous-saving growth model discussed in Solow (1956) and Swan (1956)

76 production sectors, 1 representative household and 2 primary production factors

Example: results from recursive dynamic AGE model(Potential GDP Growth)

GDP (% growth) 51-55 56-60 61-65 66-70 Avg.

Base case 4.96 4.75 4.35 3.87 4.48

High case 5.42 5.65 5.55 5.21 5.46

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• Base on Neoclassical growth theory of the Ramsey-Cass-Koopmans type

• Extension to incorporate investment adjustment cost and embodied Q theory

• Captures both macro (intertemporal) and micro (intratemporal) efficiencies

• Solve one for all period such that both intertemporal and intratemporal efficiencies are satisfied

Ramsey-Cass-Koopmans Dynamic CGE Model

• It is a dynamic CGE model for a small open-economy• Single household, 12 production sectors, one government• Solve for 100 period horizon with totally 36,988 single equations

• Can be divided into five blocks, households, firms, foreign trade, within

period equilibrium conditions and steady-state terminal conditions.

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Example: results from R-C-K Dynamic CGE Model(Impacts of Oil Prices Shock on the Thai Economy)

GDP C I G X M CPI

% change from 2003

2004 -0.156 -0.552 -1.627 3.309 -1.169 -1.814 0.400

2005 -0.554 -1.483 -2.771 7.910 -3.041 -3.883 1.159

2006 -0.866 -2.037 -3.490 10.504 -4.131 -4.951 1.616

2007 -1.027 -2.277 -3.658 11.275 -4.596 -5.358 1.816

% change from previous year

2004 -0.156 -0.552 -1.627 3.309 -1.169 -1.814 0.400

2005 -0.401 -0.940 -1.159 4.452 -1.897 -2.113 0.757

2006 -0.312 -0.563 -0.741 2.404 -1.119 -1.108 0.455

2007 -0.161 -0.240 -0.177 0.692 -0.486 -0.427 0.197

% change from previous year over 1 % change in crude oil price

2004 -0.004 -0.016 -0.047 0.096 -0.034 -0.052 0.012

2005 -0.010 -0.023 -0.028 0.109 -0.046 -0.052 0.019

2006 -0.018 -0.032 -0.043 0.137 -0.064 -0.063 0.026

2007 -0.049 -0.067 0.004 0.065 -0.131 -0.101 0.057

Average 04-07

-0.020 -0.035 -0.029 0.102 -0.069 -0.067 0.028

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GDP C I G X M CPI

Oil shock (% from previous year)

2004 -0.157 -0.630 -1.159 3.528 -1.260 -1.739 0.503

2005 -0.403 -0.953 -1.063 4.683 -1.910 -2.029 0.769

Average -0.280 -0.792 -1.111 4.106 -1.585 -1.884 0.636

Agricultural TFP shock (% from previous year)

2004 -0.860 -0.777 -0.850 -0.312 -0.928 -0.678 0.614

2005 -0.862 -0.757 -0.702 -0.348 -1.005 -0.679 0.610

Average -0.861 -0.767 -0.776 -0.330 -0.966 -0.679 0.612

Table C1: The impacts of oil and agricultural TFP shocks on aggregate variables

Sources: NESDB-RCK-CCB CGE simulation

Example: results from R-C-K Dynamic CGE Model (impacts of Oil V.S agricultural TFP shocks during 2004-2005)

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Experiences

• For NESDB, CQM is the most available efficient tool for short-run & rapid estimation (times & resources)

• Nevertheless, QFM is needed (for the purpose of both reconciliation and longer-term projection)

• Under some certain conditions (shocks to the variables that are not included in QFM, structural models (i.e. CGE models) are useful

Analytical tools for economic projections: no single tool suit for all purposes

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Experiences

Key success factors• Technical skills• Understanding of economic structures and economic

conditions• Databases, models & assumptions

Problems• To release advanced estimates (base on QFM and CQM

forecasts), their precision is the main obstacle. • Strong and abrupt shocks reduce precision of CQM

forecasts • Fast changes in global condition made it more difficult

to update exogenous variables in QFM and thus reduce its precision.

• Judgments rise with projection horizon

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Challenges

• To officially release advance estimated of quarterly GDP

• To improve/construct/ find a better econometric model for quarterly and yearly forecast

• To form an efficient forecasting team