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Bayesian Estimation of Total Investment Expenditures For Romanian Economy using DYNARE

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  • Marco Ratto
  • Riccardo Girardi

Abstract

In this paper we present the first estimation results of total investment expenditure for the Romanian economy, applying the Bayesian estimation approach of DSGE models presented in a number of various papers appeared recently in the open literature. The procedure requires the linear approximation of the original non-linear model, obtaining a LRE system, which is then solved for the reduced form state equation in its predetermined variables. Subsequently, standard Kalman recursions are applied to compute the likelihood which, combined with the prior assumptions, allows to evaluate the posterior probability. First the posterior mode is estimated, followed by a posterior simulation applying Metropolis Markov Chain Monte Carlo methods. The estimation procedure is implemented using the DYNARE software (Juillard, 1996-2003), a free available and open source software. After the model is defined and the first order conditions computed, the DSGE model is "decoded" by a parser embedded in DYNARE. All subsequent steps (linearisation, LRE system solution via generalized Schur decomposition, likelihood computation, Metropolis posterior simulation) are implemented by the software. DYNARE proved to be an extremely flexible and powerful tool, which allows to easily implement advanced Bayesian estimation techniques of DSGE models, with a considerable spare of coding and debugging time.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 151.

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Date of creation: 11 Aug 2004
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Handle: RePEc:sce:scecf4:151

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Keywords: Capital; Investment; Bayesian Analysis; General Equilibrium Models; SDGE;

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  1. Christopher A. Sims & Tao Zha, 1995. "Error bands for impulse responses," Working Paper 95-6, Federal Reserve Bank of Atlanta.
  2. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, 09.
  3. Klein, Paul, 2000. "Using the generalized Schur form to solve a multivariate linear rational expectations model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(10), pages 1405-1423, September.
  4. Peter Ireland, 1999. "A Method for Taking Models to the Data," Computing in Economics and Finance 1999 1233, Society for Computational Economics.
  5. Frank Schorfheide, 2000. "Loss function-based evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 645-670.
  6. Werner Roeger & Jan in 't Veld, 1997. "QUEST II. A Multi-Country Business Cycle and Growth Model," European Economy - Economic Papers 123, Directorate General Economic and Monetary Affairs (DG ECFIN), European Commission.
  7. 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.
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