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Identification of Unknown Common Factors: Leaders and Followers

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  • Jason Parker
  • Donggyu Sul

Abstract

This article has the following contributions. First, this article develops a new criterion for identifying whether or not a particular time series variable is a common factor in the conventional approximate factor model. Second, by modeling observed factors as a set of potential factors to be identified, this article reveals how to easily pin down the factor without performing a large number of estimations. This allows the researcher to check whether or not each individual in the panel is the underlying common factor and, from there, identify which individuals best represent the factor space by using a new clustering mechanism. Asymptotically, the developed procedure correctly identifies the factor when N and T jointly approach infinity. The procedure is shown to be quite effective in the finite sample by means of Monte Carlo simulation. The procedure is then applied to an empirical example, demonstrating that the newly developed method identifies the unknown common factors accurately.

Suggested Citation

  • Jason Parker & Donggyu Sul, 2016. "Identification of Unknown Common Factors: Leaders and Followers," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 227-239, April.
    • Handle: RePEc:taf:jnlbes:v:34:y:2016:i:2:p:227-239
    DOI: 10.1080/07350015.2015.1026439
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    Cited by:

    1. Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Klochkov, Yegor, 2022. "SONIC: SOcial Network analysis with Influencers and Communities," Journal of Econometrics, Elsevier, vol. 228(2), pages 177-220.
    2. Palenzuela, Diego Rodriguez & Saiz, Lorena & Stoevsky, Grigor & Tóth, Máté & Warmedinger, Thomas & Grigoraș, Veaceslav, 2024. "The euro area business cycle and its drivers," Occasional Paper Series 354, European Central Bank.
    3. Jiang, Pan & Perez, M. Fabricio, 2021. "Follow the leader: Index tracking with factor models," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 337-350.
    4. Ryan Greenaway‐McGrevy & Nelson C. Mark & Donggyu Sul & Jyh‐Lin Wu, 2018. "Identifying Exchange Rate Common Factors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(4), pages 2193-2218, November.
    5. Pesaran, M. Hashem & Yang, Cynthia Fan, 2021. "Estimation and inference in spatial models with dominant units," Journal of Econometrics, Elsevier, vol. 221(2), pages 591-615.
    6. Brownlees, Christian & Mesters, Geert, 2021. "Detecting granular time series in large panels," Journal of Econometrics, Elsevier, vol. 220(2), pages 544-561.
    7. Sun, Yucheng & Xu, Wen & Zhang, Chuanhai, 2023. "Identifying latent factors based on high-frequency data," Journal of Econometrics, Elsevier, vol. 233(1), pages 251-270.
    8. George Kapetanios & M. Hashem Pesaran & Simon Reese, 2018. "A Residual-based Threshold Method for Detection of Units that are Too Big to Fail in Large Factor Models," CESifo Working Paper Series 7401, CESifo.
    9. Kapetanios, G. & Pesaran, M.H. & Reese, S., 2021. "Detection of units with pervasive effects in large panel data models," Journal of Econometrics, Elsevier, vol. 221(2), pages 510-541.

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