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Trend Mis-specifications and Estimated Policy Implications in DSGE Models

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  • Varang Wiriyawit

Abstract

Extracting a trend component from nonstationary data is one of the first challenges in estimating a DSGE model. The misspecification of the component can distort structural parameter estimates and translate into a bias in policy-relevant statistic estimates. This paper investigates how important this bias is to estimated policy implications within a DSGE framework. The quantitative results suggest the bias in parameter estimates due to trend misspecification can result in significant inaccuracies in estimating statistics of interest. This then misleads policy conclusions. Particularly, a misspecified model is estimated using a deterministic-trend specification when the true process is a random-walk with drift.

Suggested Citation

  • Varang Wiriyawit, 2014. "Trend Mis-specifications and Estimated Policy Implications in DSGE Models," ANU Working Papers in Economics and Econometrics 2014-615, Australian National University, College of Business and Economics, School of Economics.
    • Handle: RePEc:acb:cbeeco:2014-615
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    File URL: https://www.cbe.anu.edu.au/researchpapers/econ/wp615.pdf
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    References listed on IDEAS

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    Cited by:

    1. 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.

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    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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