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1
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
2
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
3
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
4
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
5
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
6
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) -
9
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
12
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
14
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
15
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
24
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.
26
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
30
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
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