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Smooth Dynamic Factor Analysis With Application To The Us Term Structure Of Interest Rates

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  • Borus Jungbacker
  • Siem Jan Koopman
  • Michel Wel

Abstract

SUMMARY We consider the dynamic factor model and show how smoothness restrictions can be imposed on factor loadings by using cubic spline functions. We develop statistical procedures based on Wald, Lagrange multiplier and likelihood ratio tests for this purpose. The methodology is illustrated by analyzing a newly updated monthly time series panel of US term structure of interest rates. Dynamic factor models with and without smooth loadings are compared with dynamic models based on Nelson–Siegel and cubic spline yield curves. We conclude that smoothness restrictions on factor loadings are supported by the interest rate data and can lead to more accurate forecasts. Copyright © 2013 John Wiley & Sons, Ltd.

Suggested Citation

  • Borus Jungbacker & Siem Jan Koopman & Michel Wel, 2014. "Smooth Dynamic Factor Analysis With Application To The Us Term Structure Of Interest Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 65-90, January.
    • Handle: RePEc:wly:japmet:v:29:y:2014:i:1:p:65-90
    DOI: 10.1002/jae.2319
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    2. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Bond portfolio optimization using dynamic factor models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 128-158.
    3. Sven Otto & Nazarii Salish, 2022. "Approximate Factor Models for Functional Time Series," Papers 2201.02532, arXiv.org, revised Feb 2025.
    4. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
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    6. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    7. Michel van der Wel & Sait R. Ozturk & Dick van Dijk, 2015. "Dynamic Factor Models for the Volatility Surface," CREATES Research Papers 2015-13, Department of Economics and Business Economics, Aarhus University.
    8. Geert Mesters & Victor van der Geest & Catrien Bijleveld, 2014. "Crime, Employment and Social Welfare: an Individual-level Study on Disadvantaged Males," Tinbergen Institute Discussion Papers 14-091/III, Tinbergen Institute.
    9. Feng, Pan & Qian, Junhui, 2018. "Forecasting the yield curve using a dynamic natural cubic spline model," Economics Letters, Elsevier, vol. 168(C), pages 73-76.
    10. Ken Nyholm, 2018. "A Rotated Dynamic Nelson†Siegel Model," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 47(1), pages 113-124, February.
    11. Konstantinos Bisiotis & Stelios Psarakis & Athanasios N. Yannacopoulos, 2022. "Affine Term Structure Models: Applications in Portfolio Optimization and Change Point Detection," Mathematics, MDPI, vol. 10(21), pages 1-33, November.

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    JEL classification:

    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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