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Structural VARs, deterministic and stochastic trends: how much detrending matters for shock identification

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  • Wiriyawit Varang

    (Mahidol University International College, 999 Phutthamonthon 4 Road, Salaya, Nakhonpathom 73170, Thailand)

  • Wong Benjamin

Abstract

Detrending within structural vector autoregressions (SVAR) is directly linked to the shock identification. We investigate the consequences of trend misspecification in an SVAR using both standard real business cycle models and bi-variate SVARs as data generating processes. Our bias decomposition reveals biases arising directly from trend misspecification are not trivial when compared to other widely studied misspecifications. Misspecifying the trend also distorts impulse response functions of even the correctly detrended variable within the SVAR system. Pretesting for unit roots mitigates trend misspecification to some extent. We also find that while practitioners can specify high lag order VARs to mitigate trend misspecification, relying on common information criterion such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC) may choose a lag order that is too low.

Suggested Citation

  • Wiriyawit Varang & Wong Benjamin, 2016. "Structural VARs, deterministic and stochastic trends: how much detrending matters for shock identification," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 141-157, April.
    • Handle: RePEc:bpj:sndecm:v:20:y:2016:i:2:p:141-157:n:6
    DOI: 10.1515/snde-2015-0030
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    Cited by:

    1. Elekdag, Selim & Han, Fei, 2015. "What drives credit growth in emerging Asia?," Journal of Asian Economics, Elsevier, vol. 38(C), pages 1-13.
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    3. Giovanni Caggiano & Efrem Castelnuovo & Gabriela Nodari, 2022. "Uncertainty and monetary policy in good and bad times: A replication of the vector autoregressive investigation by Bloom (2009)," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 210-217, January.

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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