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Understanding the Sims-Cogley-Nason Approach in A Finite Sample

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Abstract

Kehoe2006 advocates that in evaluating an economic model, the Sims-Cogley-Nason (SCN) approach should be adopted in which empirical impulse responses are compared to those obtained from the identical structural VAR run on model generated data of the same length as actual observations. This paper examines, using Monte Carlo simulation, finite sample properties of the SCN approach. Throughout the paper, we use the simple textbook New-Keynesian model as data generating process, and focus on effects of the identified monetary shocks, derived by structural VAR with short-run identification assumption. We find that when the model violates the identifying restriction and monetary shocks are misidentified, the SCN approach has poor small sample performance. We show that: 1) The estimated impulse responses are biased and uninformative; 2) The parameter estimates derived by matching impulse responses are biased and with large mean square error. Ironically, the very reason calling for the SCN approach - mis-identification, is also the cause for its poor finite sample performance.

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  • Liu, Lin & Hussain, Syed, 2013. "Understanding the Sims-Cogley-Nason Approach in A Finite Sample," MPRA Paper 53118, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:53118
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    More about this item

    Keywords

    Sims-Cogley-Nason Approach; Finite Sample Property; Structural VAR; New-Keynesian Model; Monetary Policy Shocks.;
    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
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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