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Heterogeneity in Manufacturing Growth Risk

Author

Listed:
  • Daan Opschoor

    (Erasmus University Rotterdam)

  • Dick van Dijk

    (Erasmus University Rotterdam)

  • Philip Hans Franses

    (Erasmus University Rotterdam)

Abstract

We analyze output growth risk with respect to financial conditions across U.S. manufacturing industries. Using a multi-level quantile regression approach, we find strong heterogeneity in growth risk, particularly between the more vulnerable durable goods sector and the more resilient nondurable goods sector. Moreover, we show that industry characteristics significantly explain these differences. Large, or material intensive durable goods producing, or energy intensive nondurable goods producing industries are more vulnerable to adverse financial conditions, while industries engaging in labor hoarding, or with a high capital or overhead labor intensity are less susceptible.

Suggested Citation

  • Daan Opschoor & Dick van Dijk & Philip Hans Franses, 2021. "Heterogeneity in Manufacturing Growth Risk," Tinbergen Institute Discussion Papers 21-036/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20210036
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    References listed on IDEAS

    as
    1. Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.
    2. Antonio F Galvao & Ted Juhl & Gabriel Montes-Rojas & Jose Olmo, 2018. "Testing Slope Homogeneity in Quantile Regression Panel Data with an Application to the Cross-Section of Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 211-243.
    3. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
    4. Kengo Kato, 2012. "Asymptotic normality of Powell’s kernel estimator," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(2), pages 255-273, April.
    5. Jungmo Yoon & Antonio F. Galvao, 2020. "Cluster robust covariance matrix estimation in panel quantile regression with individual fixed effects," Quantitative Economics, Econometric Society, vol. 11(2), pages 579-608, May.
    6. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    7. Galvao, Antonio F. & Wang, Liang, 2015. "Efficient minimum distance estimator for quantile regression fixed effects panel data," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 1-26.
    8. Tomohiro Ando & Jushan Bai, 2020. "Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 266-279, January.
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    More about this item

    Keywords

    downside risk; business cycle; quantile regression; manufacturing; financial conditions;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • L16 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Industrial Organization and Macroeconomics; Macroeconomic Industrial Structure
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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