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Selection Of The Number Of Factors In Presence Of Structural Instability: A Monte Carlo Study

Author

Listed:
  • Charles Olivier Mao Takongmo

    (McGill University)

  • Dalibor Stevanovic

    (Université du Québec à Montréal)

Abstract

In this paper we study the selection of the number of primitive shocks in exact and approximate factor models in the presence of structural instability. The empirical analysis shows that the estimated number of factors varies substantially across several selection methods and over the last 30 years in standard large macroeconomic and financial panels. Using Monte Carlo simulations, we suggest that the structural instability, expressed as time-varying factor loadings, can alter the estimation of the number of factors and therefore provides an explanation for the empirical findings.

Suggested Citation

  • Charles Olivier Mao Takongmo & Dalibor Stevanovic, 2015. "Selection Of The Number Of Factors In Presence Of Structural Instability: A Monte Carlo Study," L'Actualité Economique, Société Canadienne de Science Economique, vol. 91(1-2), pages 177-233.
    • Handle: RePEc:ris:actuec:0118
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    Cited by:

    1. Marine Carrasco & Barbara Rossi, 2016. "In-Sample Inference and Forecasting in Misspecified Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 313-338, July.
    2. Olivier Fortin‐Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "A large Canadian database for macroeconomic analysis," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(4), pages 1799-1833, November.
    3. Mao Takongmo, Charles-O. & Touré, Adam, 2023. "Trade openness and connectedness of national productions: Do financial openness, economic specialization, and the size of the country matter?," Economic Modelling, Elsevier, vol. 125(C).
    4. Maxime Leroux & Rachidi Kotchoni & Dalibor Stevanovic, 2017. "Forecasting economic activity in data-rich environment," EconomiX Working Papers 2017-5, University of Paris Nanterre, EconomiX.
    5. Charles Olivier Mao Takongmo, 2021. "DSGE models, detrending, and the method of moments," Bulletin of Economic Research, Wiley Blackwell, vol. 73(1), pages 67-99, January.
    6. Qin Zhang & He Ni & Hao Xu, 2023. "Forecasting models for the Chinese macroeconomy in a data‐rich environment: Evidence from large dimensional approximate factor models with mixed‐frequency data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 719-767, March.
    7. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic forecast accuracy in a data‐rich environment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1050-1072, November.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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