Economic Projection at NESDB

1. 1 Economic Projection at NESDB International Seminar on Timeliness Methodology and Comparability of Rapid Estimates of Economic Trends Ottawa, Canada 27-29 May 2009 Wichayayuth Boonchit Economic Modeling & Projection Division Macroeconomic Office Office of the National Economic & Social Development Board Thailand wichayayuth@nesdb.go.th

2. 2 NESDB�s Functions and Responsibilities To study and analyze the national economic and social condition for development planning, and recommend related policy issues to the government 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

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6. 6 Tools for Quarterly and Yearly Economic Projection

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8. 8 Yearly GDP Projection

9. 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). Current Quarter Model: CQMTool for rapid estimation CQM the tool for rapid estimation CQM is first developed by Professor Lawrence Kline In principle, the model use high frequency information (or indicator variable) to estimate the future value of GDP both demand and supply sides High frequency data or indicator variable can be daily weakly or monthly indicators but up to date in Thailand we use only monthly indicator. How the model work? Actually the model establish the relationship between high frequency data with low frequency data. In other words the model establish the relation ship between indicator variables and National Income and Production Account variables. This relationship between low and high frequency data is called bridge equation. Once they establish bridge equation, the model will forecast future value of indicator variables for 6 month by applying Box Jengin Process. The forecasted value of indicator variable is then used to forecast NIPA variables by using bridge equation. The model will be updated on the rolling basis, that is when new pieces of information become available. In the case of Thailand, we update every month as we use monthly indicator variables. CQM the tool for rapid estimation CQM is first developed by Professor Lawrence Kline In principle, the model use high frequency information (or indicator variable) to estimate the future value of GDP both demand and supply sides High frequency data or indicator variable can be daily weakly or monthly indicators but up to date in Thailand we use only monthly indicator. How the model work? Actually the model establish the relationship between high frequency data with low frequency data. In other words the model establish the relation ship between indicator variables and National Income and Production Account variables. This relationship between low and high frequency data is called bridge equation. Once they establish bridge equation, the model will forecast future value of indicator variables for 6 month by applying Box Jengin Process. The forecasted value of indicator variable is then used to forecast NIPA variables by using bridge equation. The model will be updated on the rolling basis, that is when new pieces of information become available. In the case of Thailand, we update every month as we use monthly indicator variables.

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12. 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)

13. 13 Example: relationship between indicator variables and NIPA variables

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16. 16 This slide shows timeframe of the forecast. For example, at the end of May, the actual GDP of the first quarter is released. On the 15 of June, the actual value of indicator variables is available. Then we update the model with new piece of information of indicator variable. The model will project the value of indicator variable for 5 months, for the rest of second quarter and third quarter. Then it use the relationship that is established in Bridge equation to estimate GDP in the second and third quarter. This slide shows timeframe of the forecast. For example, at the end of May, the actual GDP of the first quarter is released. On the 15 of June, the actual value of indicator variables is available. Then we update the model with new piece of information of indicator variable. The model will project the value of indicator variable for 5 months, for the rest of second quarter and third quarter. Then it use the relationship that is established in Bridge equation to estimate GDP in the second and third quarter.

17. 17 NIPA & Indicator Variables (A) Expenditure Side

18. 18 Similarly, this slide show the underlined indicator variable of each GDP component on the supply sideSimilarly, this slide show the underlined indicator variable of each GDP component on the supply side

19. 19 Example: CQM Forecast for Q1/2009

20. 20 Quarterly Financial Model: QFM

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23. 23 Tools for Impact Analysis and Medium & Long-term Projections

24. 24 Recursive Dynamic AGE model

25. 25 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. The model develop in this chapter share the basic nature with other dynamic CGE model found in the literature. Overall, it is formulated along the line with the neoclassical growth theory of the Ramsey and Koopman with the inclusion convex cost of investment that finally lead to the inclusion of investment Q theory. This type of model capture both intra and intertemporal allocation efficiency. Therefore it solve one for all period in model horizon. By its nature, it is a dynamic CGE model of a small-open economy. The model contains 12 production sector a single representative household. The model can be divided into 5 main blocks, household, firm, foreign trade equilibrium condition and steady state condition. The model develop in this chapter share the basic nature with other dynamic CGE model found in the literature. Overall, it is formulated along the line with the neoclassical growth theory of the Ramsey and Koopman with the inclusion convex cost of investment that finally lead to the inclusion of investment Q theory. This type of model capture both intra and intertemporal allocation efficiency. Therefore it solve one for all period in model horizon. By its nature, it is a dynamic CGE model of a small-open economy. The model contains 12 production sector a single representative household. The model can be divided into 5 main blocks, household, firm, foreign trade equilibrium condition and steady state condition.

26. 26 Example: results from R-C-K Dynamic CGE Model(Impacts of Oil Prices Shock on the Thai Economy) This slide show the impacts of oil prices shock on the aggregate economy. The result show that oil prices shock lower GDP by around 0.16 percent in 2004 close to the result from static analysis. However, as oil prices accelerate, it reduces GDP further by around 1 percent in 2006 and 2007. Since the shock in 2006 and 2007 are set at the same level, the different between GDP contraction in this two period is the role of dynamic property of the economy. Note that the result from dynamic model cannot be directly compare to the result from static model. This is the models differ in in their structure and assumption that underlined simulation scenarios. However, dynamic model show a stronger negative impact on investment than static model but the less negative impact on investment. One possible explanation is that in dynamic model agents are looking forward agents. Given that the price level tend to increase further, household tend to consume more in current period and less in the future. Similarly, firms cut there investment expenditure in current period taking into account the economic situation in both current and future period. Government income increase. With the assumption of fixed government saving, government consumption increase throughout the period of the shock. This assumption is chosen to reflect the real situation in 2004-2005 when the government prefer to spend all income in favor of social policy in order to gain popularity. In addition in the early period of the shock, some analyst used to quote the increase of government tax revenue as a positive indication of economic performance. However, our simulation shows that such interpretation is misleading. In the presence of oil prices shock taxes income can increase concurrently with GDP contraction. Both real export and real import decrease. Net real export increase in the first period and contribute to GDP as found in static analysis. However, as oil prices accelerate the net real export decrease and reinforce the contraction of other GDP component. In dynamic model, CPI is no longer fixed nemeriar. Therefore the result is easier to interpret. The simulation result show that CPI increases by around 0.24 percent in 2004 and steeply rise to around 1.5 percent when the oil prices reach their peak. This slide show the impacts of oil prices shock on the aggregate economy. The result show that oil prices shock lower GDP by around 0.16 percent in 2004 close to the result from static analysis. However, as oil prices accelerate, it reduces GDP further by around 1 percent in 2006 and 2007. Since the shock in 2006 and 2007 are set at the same level, the different between GDP contraction in this two period is the role of dynamic property of the economy. Note that the result from dynamic model cannot be directly compare to the result from static model. This is the models differ in in their structure and assumption that underlined simulation scenarios. However, dynamic model show a stronger negative impact on investment than static model but the less negative impact on investment. One possible explanation is that in dynamic model agents are looking forward agents. Given that the price level tend to increase further, household tend to consume more in current period and less in the future. Similarly, firms cut there investment expenditure in current period taking into account the economic situation in both current and future period. Government income increase. With the assumption of fixed government saving, government consumption increase throughout the period of the shock. This assumption is chosen to reflect the real situation in 2004-2005 when the government prefer to spend all income in favor of social policy in order to gain popularity. In addition in the early period of the shock, some analyst used to quote the increase of government tax revenue as a positive indication of economic performance. However, our simulation shows that such interpretation is misleading. In the presence of oil prices shock taxes income can increase concurrently with GDP contraction. Both real export and real import decrease. Net real export increase in the first period and contribute to GDP as found in static analysis. However, as oil prices accelerate the net real export decrease and reinforce the contraction of other GDP component. In dynamic model, CPI is no longer fixed nemeriar. Therefore the result is easier to interpret. The simulation result show that CPI increases by around 0.24 percent in 2004 and steeply rise to around 1.5 percent when the oil prices reach their peak.

27. 27 Example: results from R-C-K Dynamic CGE Model (impacts of Oil V.S agricultural TFP shocks during 2004-2005)

28. 28 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

29. 29 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. 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