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Structural VARs, deterministic and stochastic trends: Does detrending matter?

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Abstract

We highlight how detrending within Structural Vector Autoregressions (SVAR) is directly linked to the shock identification. Consequences of trend misspecification are investigated using a prototypical Real Business Cycle model as the Data Generating Process. Decomposing the different sources of biases in the estimated impulse response functions, we find the biases arising directly from trend misspecification are not trivial when compared to other widely studied misspecifications. Misspecifying the trend can also distort impulse response functions of even the correctly detrended variable within the SVAR system. A possible solution hinted by our analysis is that increasing the lag order when estimating the SVAR may mitigate some of the biases associated with trend misspecification.

Suggested Citation

  • Benjamin Wong & Varang Wiriyawit, 2015. "Structural VARs, deterministic and stochastic trends: Does detrending matter?," Reserve Bank of New Zealand Discussion Paper Series DP2015/02, Reserve Bank of New Zealand.
    • Handle: RePEc:nzb:nzbdps:2015/02
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    Cited by:

    1. Giovanni Caggiano & Efrem Castelnuovo & Gabriela Nodari, 2020. "Uncertainty and Monetary Policy in Good and Bad Times: A Replication of the VAR Investigation by Bloom (2009)," CAMA Working Papers 2020-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. 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.
    3. Elekdag, Selim & Han, Fei, 2015. "What drives credit growth in emerging Asia?," Journal of Asian Economics, Elsevier, vol. 38(C), pages 1-13.

    More about this item

    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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