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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 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) 2 3 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 4 Off-model information Data Example External estimates Macroeconomic Advisers / Blue Chip Economic of variables Indicators estimates of GDP High-frequency observations Monthly CPI—as indicator for quarterly CPI on model variables High-frequency indicators of model variables Large data sets Monthly Private Investment Index—as indicator for quarterly private investment Indicators of output, employment, wages, consumption, investment, interest rates, money, credit, prices, etc.— to be incorporated in dynamic factor models 5 Boivin and Giannone (2006) 1. Model 3. Model in data-rich environment F = subset of variables for which large number of indicators X are observable Here we impose DSGE model on transition equations of latent factors 2. Model solution Measurement equations with with 6 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 7 Benes et al. (2009) Real-time problem Data partially available as of beginning of quarter Q Options for incorporating extraneous information 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) X indicates data available 4. “Soft tunes” – Incorporate off-model predictions consistently with model structure through measurement equations using Kalman filter 8 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