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Estimation methods comparison of SVAR model with the mixture of two normal distributions - Monte Carlo analysis

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  • Katarzyna Maciejowska

Abstract

This paper addresses the issue of obtaining maximum likelihood estimates of parameters for structural VAR models with a mixture of distributions. Hence the problem does not have a closed form solution, numerical optimization procedures need to be used. A Monte Carlo experiment is design to compare the performance of four maximization algorithms and two estimation strategies. It is shown that the EM algorithm outperforms the general maximization algorithms such as BFGS, NEWTON and BHHH. Moreover simplification of the probelm introduced in the two steps quasi ML method does not worsen small sample properties of the estimators and therefore may be recommended in the empirical analysis.

Suggested Citation

  • Katarzyna Maciejowska, 2010. "Estimation methods comparison of SVAR model with the mixture of two normal distributions - Monte Carlo analysis," Economics Working Papers ECO2010/27, European University Institute.
  • Handle: RePEc:eui:euiwps:eco2010/27
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    Cited by:

    1. Paolo Guarda & Abdelaziz Rouabah & John Theal, 2011. "An MVAR Framework to Capture Extreme Events in Macroprudential Stress Tests," BCL working papers 63, Central Bank of Luxembourg.
    2. Sun, Hang, 2016. "Crisis-Contingent Dynamics of Connectedness: An SVAR-Spatial-Network “Tripod” Model with Thresholds," Research Memorandum 032, Maastricht University, Graduate School of Business and Economics (GSBE).

    More about this item

    Keywords

    Structural vetcor autoregression ; Error correction models; Mixed normal; Monte Carlo;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

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