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Estimating DSGE Models under Partial Information

  • Paul Levine
  • Joseph Pearlman
  • George Perendia

Most DSGE models and methods make inappropriate asymmetric information assumptions. They assume that all economic agents have full access to measurement of all variables and past shocks, whereas the econometricians have no access to this. An alternative assumption is that there is symmetry, in that the information set available to both agents and econometricians is incomplete. The reality lies somewhere between the two, because agents are likely to be subject to idiosyncratic shocks which they can observe, but are unable to observe other agents’ idiosyncratic shocks, as well as being unable to observe certain economy-wide shocks; however such assumptions generally lead to models that have no closed-form solution. This research aims to compare the two alternatives - the asymmetric case,as commonly used in the literature, and the symmetric case, which uses the partial information solution of Pearlman et al. (1986) using standard EU datasets. We use Bayesian MCMC methods, with log-likelihoods accounting for partial information.The work then extends the data to allow for a greater variety of measurements, and evaluates the effect on estimates, along the lines of work by Boivin and Giannoni (2005).

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Paper provided by Centre for Dynamic Macroeconomic Analysis in its series CDMA Working Paper Series with number 200722.

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Date of creation: 15 Nov 2007
Date of revision:
Handle: RePEc:san:cdmawp:0722
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  1. Pearlman, Joseph & Currie, David & Levine, Paul, 1986. "Rational expectations models with partial information," Economic Modelling, Elsevier, vol. 3(2), pages 90-105, April.
  2. Lucas, Robert E, Jr, 1975. "An Equilibrium Model of the Business Cycle," Journal of Political Economy, University of Chicago Press, vol. 83(6), pages 1113-44, December.
  3. Nicoletta Batini & Alejandro Justiniano & Paul Levine & Joseph Pearlman, 2004. "Robust Inflation-Forecast-Based Rules to Shield against Indeterminacy," School of Economics Discussion Papers 0804, School of Economics, University of Surrey.
  4. Jean Boivin & Marc Giannoni, 2006. "DSGE Models in a Data-Rich Environment," NBER Working Papers 12772, National Bureau of Economic Research, Inc.
  5. Collard, Fabrice & Dellas, Harris, 2004. "The New Keynesian Model with Imperfect Information and Learning," IDEI Working Papers 273, Institut d'Économie Industrielle (IDEI), Toulouse.
  6. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
  7. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
  8. Joseph G. Pearlman & Thomas J. Sargent, 2005. "Knowing the Forecasts of Others," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 480-497, April.
  9. Blanchard, Olivier Jean & Kahn, Charles M, 1980. "The Solution of Linear Difference Models under Rational Expectations," Econometrica, Econometric Society, vol. 48(5), pages 1305-11, July.
  10. Townsend, Robert M, 1983. "Forecasting the Forecasts of Others," Journal of Political Economy, University of Chicago Press, vol. 91(4), pages 546-88, August.
  11. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
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