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Large-dimensional cointegrated threshold factor models: The Global Term Structure of Interest Rates

Author

Listed:
  • Paulo M.M. Rodrigues
  • Daniel Abreu

Abstract

We extend the two-level factor model to account for cointegration between groupspecific factors in large datasets. We propose two nonlinear specifications: (i) a threshold vector error correction model (VECM) that accounts for asymmetric responses across regimes; and (ii) a band VECM that captures discontinuous state-dependent adjustment which activates only when deviations from equilibrium exceed a certain threshold. We examine the small-sample performance of both models through Monte Carlo simulations. In an empirical application, we estimate a band factor VECM on a panel of government bond yields from multiple countries, estimating one global factor and two group-specific factors associated with long- and short-term maturities. The results provide evidence of a discontinuous adjustment in the global term structure of interest rates.

Suggested Citation

  • Paulo M.M. Rodrigues & Daniel Abreu, 2025. "Large-dimensional cointegrated threshold factor models: The Global Term Structure of Interest Rates," Working Papers w202528, Banco de Portugal, Economics and Research Department.
  • Handle: RePEc:ptu:wpaper:w202528
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    References listed on IDEAS

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    1. James D. Hamilton & Xinwei Ma & Jin Xi, 2024. "Principal Component Analysis for a Mix of Stationary and Nonstationary Variables," NBER Working Papers 32068, National Bureau of Economic Research, Inc.
    2. Forni, Mario & Giannone, Domenico & Lippi, Marco & Reichlin, Lucrezia, 2009. "Opening The Black Box: Structural Factor Models With Large Cross Sections," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1319-1347, October.
    3. George Kapetanios & Massimiliano Marcellino, 2009. "A parametric estimation method for dynamic factor models of large dimensions," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(2), pages 208-238, March.
    4. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    5. Cristadoro, Riccardo & Forni, Mario & Reichlin, Lucrezia & Veronese, Giovanni, 2005. "A Core Inflation Indicator for the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 539-560, June.
    6. Lorenzo Trapani, 2018. "A Randomized Sequential Procedure to Determine the Number of Factors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1341-1349, July.
    7. Ang, Andrew & Piazzesi, Monika, 2003. "A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 745-787, May.
    8. Guerello, Chiara & Tronzano, Marco, 2020. "“Global factors, international spillovers, and the term structure of interest rates: New evidence for Asian Countries”," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    9. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    10. Christiane Baumeister & Luca Benati, 2013. "Unconventional Monetary Policy and the Great Recession: Estimating the Macroeconomic Effects of a Spread Compression at the Zero Lower Bound," International Journal of Central Banking, International Journal of Central Banking, vol. 9(2), pages 165-212, June.
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    More about this item

    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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