Publication: Structural VARs, deterministic and stochastic trends: How much detrending matters for shock identification
Issued Date
2016-04-01
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ISSN
15583708
10811826
10811826
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2-s2.0-84964721821
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Mahidol University
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SCOPUS
Bibliographic Citation
Studies in Nonlinear Dynamics and Econometrics. Vol.20, No.2 (2016), 141-157
Suggested Citation
Varang Wiriyawit, Benjamin Wong Structural VARs, deterministic and stochastic trends: How much detrending matters for shock identification. Studies in Nonlinear Dynamics and Econometrics. Vol.20, No.2 (2016), 141-157. doi:10.1515/snde-2015-0030 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/43618
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Title
Structural VARs, deterministic and stochastic trends: How much detrending matters for shock identification
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Abstract
©2016 by De Gruyter. 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.