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An observation regarding Hamilton’s recent criticisms of Kilian’s global real economic activity index

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  • Nonejad, Nima

Abstract

Hamilton (2019) offers several criticisms of the global real economic activity (REA) index suggested in Kilian (2009). This study deals with Hamilton (2019)’s arguments regarding the ability of the index to forecast commodity prices. Using in-sample population-level predictability tests, Hamilton (2019) argues that world industrial production index growth rate performs better than Kilian’s REA Index. We show that from an out-of-sample population-level predictability perspective, the REA index performs just as well if not better than world industrial production index. The Clark and West (2007) no population-level predictability null hypothesis is rejected at the same rate if not higher for the REA index, especially as the forecast horizon increases.

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  • Nonejad, Nima, 2020. "An observation regarding Hamilton’s recent criticisms of Kilian’s global real economic activity index," Economics Letters, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:ecolet:v:196:y:2020:i:c:s0165176520303517
    DOI: 10.1016/j.econlet.2020.109582
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    1. Kilian, Lutz, 2022. "Facts and fiction in oil market modeling," Energy Economics, Elsevier, vol. 110(C).
    2. Kilian, Lutz, 2019. "Measuring global real economic activity: Do recent critiques hold up to scrutiny?," Economics Letters, Elsevier, vol. 178(C), pages 106-110.
    3. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507, Elsevier.
    4. Paye, Bradley S., 2012. "‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables," Journal of Financial Economics, Elsevier, vol. 106(3), pages 527-546.
    5. Kilian, Lutz & Zhou, Xiaoqing, 2018. "Modeling fluctuations in the global demand for commodities," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 54-78.
    6. Lo, Andrew W & MacKinlay, A Craig, 1990. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 3(3), pages 431-467.
    7. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    8. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    9. Foster, F Douglas & Smith, Tom & Whaley, Robert E, 1997. "Assessing Goodness-of-Fit of Asset Pricing Models: The Distribution of the Maximal R-Squared," Journal of Finance, American Finance Association, vol. 52(2), pages 591-607, June.
    10. Xiaoqing Zhou, 2020. "Refining the workhorse oil market model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 130-140, January.
    11. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    12. Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, American Economic Association, vol. 109(5), pages 1873-1910, May.
    13. Liu, Li & Ma, Feng & Wang, Yudong, 2015. "Forecasting excess stock returns with crude oil market data," Energy Economics, Elsevier, vol. 48(C), pages 316-324.
    14. James D. Hamilton, 2019. "Measuring Global Economic Activity," NBER Working Papers 25778, National Bureau of Economic Research, Inc.
    15. Lutz Kilian & Daniel P. Murphy, 2014. "The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 454-478, April.
    16. Gargano, Antonio & Timmermann, Allan, 2014. "Forecasting commodity price indexes using macroeconomic and financial predictors," International Journal of Forecasting, Elsevier, vol. 30(3), pages 825-843.
    17. Nima Nonejad, 2020. "A detailed look at crude oil price volatility prediction using macroeconomic variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1119-1141, November.
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    Cited by:

    1. Kilian, Lutz, 2022. "Facts and fiction in oil market modeling," Energy Economics, Elsevier, vol. 110(C).
    2. Alsalman, Zeina & Herrera, Ana María & Rangaraju, Sandeep Kumar, 2023. "Oil news shocks and the U.S. stock market," Energy Economics, Elsevier, vol. 126(C).
    3. Zeina Alsalman, 2023. "Oil price shocks and US unemployment: evidence from disentangling the duration of unemployment spells in the labor market," Empirical Economics, Springer, vol. 65(1), pages 479-511, July.
    4. Nonejad, Nima, 2021. "The price of crude oil and (conditional) out-of-sample predictability of world industrial production," Journal of Commodity Markets, Elsevier, vol. 23(C).

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    More about this item

    Keywords

    Commodity prices; Global economic activity; Out-of-sample forecast evaluation; Population-level predictability;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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