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Generalized Two-Step Maximum Likelihood Estimation of Structural Vector Autoregressive Models partially identified with Short-Run Restrictions

Author

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  • Kyungho Jang
Abstract
This paper presents a generalized two-step maximum likelihood estimation method for partially identified vector autoregressive models. We suggest a likelihood ratio test for over-identification in a sub-system and derive the asymptotics for impulse responses and forecast-error variance decomposition for partially identified models. As an application, we consider an open economy model to investigate the effects of monetary policy on exchange rates and term structures. We find that exchange rates tend to overshoot and term structures have hump-shaped responses to monetary policy shocks

Suggested Citation

  • Kyungho Jang, 2004. "Generalized Two-Step Maximum Likelihood Estimation of Structural Vector Autoregressive Models partially identified with Short-Run Restrictions," Econometric Society 2004 Far Eastern Meetings 569, Econometric Society.
    • Handle: RePEc:ecm:feam04:569
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    Cited by:

    1. Jang, Kyungho, 2006. "An alternative approach to estimation of structural vector error correction models with long-run restrictions," Economics Letters, Elsevier, vol. 90(1), pages 126-131, January.

    More about this item

    Keywords

    ML estimation; VAR model; Identification; Likelihood ratio test; Asymptotic distribution; Impulse response; Forecast-error variance decomposition; Monetary policy; Exchange rate;
    All these keywords.

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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