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Correlated Errors Challenge Vulnerable Growth

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  • Wei Long
  • Liang Peng
  • Bingduo Yang

Abstract

We examine the impact of correlated errors on findings regarding fluctuations in US GDP growth, which are primarily driven by the lower quantiles of the distribution as predicted by financial conditions. A proposed zero‐correlation test reveals the presence of correlated residuals in the quantile regression, introducing endogeneity and raising concerns about the validity of the previous analysis. In response, we extend the model by including two additional lags of predictors. The revised specification shows that financial conditions alone no longer significantly predict lower‐tail GDP growth, although their joint effect with lagged values remains significant. These results challenge earlier conclusions about the sensitivity of GDP growth to financial conditions.

Suggested Citation

  • Wei Long & Liang Peng & Bingduo Yang, 2025. "Correlated Errors Challenge Vulnerable Growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(6), pages 715-721, September.
    • Handle: RePEc:wly:japmet:v:40:y:2025:i:6:p:715-721
    DOI: 10.1002/jae.3136
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    References listed on IDEAS

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    1. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    2. Yuzhu Tian & Manlai Tang & Yanchao Zang & Maozai Tian, 2018. "Quantile regression for linear models with autoregressive errors using EM algorithm," Computational Statistics, Springer, vol. 33(4), pages 1605-1625, December.
    3. Roger Koenker & Zhijie Xiao, 2004. "Unit Root Quantile Autoregression Inference," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 775-787, January.
    4. Antonio F. Galvao JR. & Gabriel Montes-Rojas & Sung Y. Park, 2013. "Quantile Autoregressive Distributed Lag Model with an Application to House Price Returns," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 307-321, April.
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