Testing For Threshold Nonlinearity in Short-Term Interest Rates
AbstractThis article addresses some empirical problems in the term structure of interest rates using a threshold autoregressive framework with GARCH errors. This framework provides a parsimonious representation of some stylized features of interest rate data and facilitates statistical inference in the presence of high persistence and conditional heteroskedasticity. We propose a bootstrap-based LM test for linearity in the conditional mean and variance functions. The empirical results indicate a presence of threshold nonlinearities in the AR and GARCH representations of the conditional moments of short-term rate. The explicit modeling of these nonlinearities appears to improve the stability properties of the process for spot rate. The article also reports that allowing for threshold nonlinearities in conditional mean and variance leads to significant forecast improvements. The economic significance of these findings is evaluated by the term structure implications of the estimated TAR-GARCH model. Copyright 2005, Oxford University Press.
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Bibliographic InfoArticle provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.
Volume (Year): 3 (2005)
Issue (Month): 3 ()
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- Peter Sephton & Janelle Mann, 2013. "Threshold Cointegration: Model Selection with an Application," Journal of Economics and Econometrics, Economics and Econometrics Research Institute (EERI), Brussels, vol. 56(2), pages 54-77.
- Gospodinov, Nikolay, 2008. "Asymptotic and bootstrap tests for linearity in a TAR-GARCH(1,1) model with a unit root," Journal of Econometrics, Elsevier, vol. 146(1), pages 146-161, September.
- Chew Lian Chua & Sandy Suardi & Sarantis Tsiaplias, 2011. "Predicting Short-Term Interest Rates: Does Bayesian Model Averaging Provide Forecast Improvement?," Melbourne Institute Working Paper Series wp2011n01, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
- Chen, Cathy W.S. & Gerlach, Richard H. & Tai, Amanda P.J., 2008. "Testing for nonlinearity in mean and volatility for heteroskedastic models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(3), pages 489-499.
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