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Impulse Response Functions in the Dynamic Stochastic General Equilibrium Vector Autoregression Model

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  • Renata Wróbel-Rotter

    (Cracow University of Economics)

Abstract

The model considered in the paper is defined as VAR with the prior distribution for parameters generated by the dynamic stochastic general equilibrium (DSGE) model. The degree of economic restrictions in the DSGE-VAR model is controlled by the weighting parameter. In the paper there is investigated the impact of the weighting parameter prior specifications for the posterior shape of impulse response functions (IRFs). In case of conditional models the paths of IRFs highly depend on the value of the weighting parameter that is set arbitrary. When considering full estimation with different prior types, means and gradual change in the dispersion the posterior time paths of IRFs are similar in models with high values of the marginal data density.

Suggested Citation

  • Renata Wróbel-Rotter, 2016. "Impulse Response Functions in the Dynamic Stochastic General Equilibrium Vector Autoregression Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(2), pages 93-114, June.
    • Handle: RePEc:psc:journl:v:8:y:2016:i:2:p:93-114
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    References listed on IDEAS

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    More about this item

    Keywords

    dynamic stochastic general equilibrium vector autoregression; DSGE-VAR; impulse response functions; marginal;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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