A Panel Discussion on Recent Developments and Issues on DSGE Modeling by Surach Tanboon Monetary Policy Department Bank of Thailand Presented at the SEACEN-CCBS/BOE-BSP Workshop on Dynamic Stochastic General Equilibrium Modeling and Econometric Techniques November 23–27, 2009 Manila, Philippines

2222 Recent Workhops on DSGE Modeling  CCBS/Bank of England (June 2009) Research Forum: Recent Developments in DSGE Models –“Incorporating Conjunctural Analysis in Structural Models”* –Contribution Methodology to incorporate monthly timely information in estimated quarterly structural DSGE models  Research Department/IMF (November 2009) Workshop: Forecasting with Structural Models and Real-Time Indicators –“Structural Models in Real Time”** –Contribution Methodology to combine extraneous predictions in a way consistent with underlying model structure *Giannoni, Monti, and Reichlin (2009) **Benes, Clinton, Johnson, Laxton, and Matheson (2009)

3333 Motivation: Why off-model information might be needed?  When estimate DSGE models, we have assumed all information can be summarized in small data set –True only when model is well specified / model variables are observed  In practice, need potentially informative data/indicators to cope with:  Unobserved model’s concepts e.g., total factor productivity  Imperfectly measured variables e.g., aggregate price measured by CPI, PCE deflator, GDP deflator

4444 Off-model information DataExample External estimates of variables Macroeconomic Advisers / Blue Chip Economic Indicators estimates of GDP High-frequency observations on model variables Monthly CPI—as indicator for quarterly CPI High-frequency indicators of model variables Monthly Private Investment Index—as indicator for quarterly private investment Large data sets Indicators of output, employment, wages, consumption, investment, interest rates, money, credit, prices, etc.— to be incorporated in dynamic factor models

5555 Boivin and Giannone (2006) 1. Model 2. Model solution with 3. Model in data-rich environment Measurement equations with Here we impose DSGE model on transition equations of latent factors F = subset of variables for which large number of indicators X are observable

6666 Boivin and Giannone (2006) Case 1 No extraneous information (Smets and Wouters, 2004): Seven model variables are perfectly observed Case 2 With extraneous information: Case 1 + seven new indicators of F

7777 Benes et al. (2009) Real-time problem Data partially available as of beginning of quarter Q 1.Data set truncation 2.Pure model forecast  Use all available quarterly data  Solve model for missing obs. 3.“Hard tunes”  Use estimates obtained outside model to fill in for missing data  Leeper’s critique (2003) 4.“Soft tunes” –Incorporate off-model predictions consistently with model structure through measurement equations using Kalman filter Options for incorporating extraneous information X indicates data available

8888 Soft-tuning Model solution Expanded state-space solution  is extraneous predictions that contain information relating to  These predictions can be made at any point during the quarter  Allowing update of estimates each quarter