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(Almost) Recursive Shock Identification with Economic Parameter Restrictions

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  • Jan Pablo Burgard
  • Matthias Neuenkirch
  • Dennis Umlandt

Abstract

We propose to estimate the parameters of a vector autoregressive model under the restriction that economic theory is not violated, while the shocks are still recursively identified. We use an augmented Lagrange solution approach, which adjusts the coefficients to meet the theoretical requirements. In a generalization, we additionally allow for a (minimal) rotation of the Cholesky matrix. Based on a Monte Carlo study and an empirical application, we show that the "almost recursive identification with parameter restrictions" leads to a solution that avoids an estimation bias, generates theory-consistent impulse responses, and is as close as possible to the recursive scheme.

Suggested Citation

  • Jan Pablo Burgard & Matthias Neuenkirch & Dennis Umlandt, 2023. "(Almost) Recursive Shock Identification with Economic Parameter Restrictions," Research Papers in Economics 2023-01, University of Trier, Department of Economics.
  • Handle: RePEc:trr:wpaper:202301
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    References listed on IDEAS

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    1. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
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    5. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148, Elsevier.
    6. Jing Cynthia Wu & Fan Dora Xia, 2016. "Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 253-291, March.
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    More about this item

    Keywords

    Non-Linear Optimization; Recursive Identification; Rotation; Sign Restrictions;
    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
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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