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Long-Lag VARs

Author

Listed:
  • De Graeve, Ferre

    (KU Leuven)

  • Westermark, Andreas

    (Research Department, Central Bank of Sweden)

Abstract

Macroeconomic research often relies on structural vector autoregressions, (S)VARs, to uncover empirical regularities. Critics argue the method goes awry due to lag truncation: short lag-lengths imply a poor approximation to important data-generating processes (e.g. DSGE-models). Empirically, short lag-length is deemed necessary as increased parametrization induces excessive uncertainty. The paper shows that this argument is incomplete. Longer lag-length simultaneously reduces misspecification, which in turn reduces variance. For data generated by frontier DSGE-models long-lag VARs are feasible, reduce bias and variance, and have better coverage. Long-lag VARs are also viable in common macroeconomic data and applications. Thus, contrary to conventional wisdom, the trivial solution to the critique actually works.

Suggested Citation

  • De Graeve, Ferre & Westermark, Andreas, 2025. "Long-Lag VARs," Working Paper Series 451, Sveriges Riksbank (Central Bank of Sweden).
    • Handle: RePEc:hhs:rbnkwp:0451
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    More about this item

    Keywords

    VAR; SVAR; Lag-length; Lag truncation;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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