Modeling and Forecasting Short-term Interest Rates: The Benefits of Smooth Regimes, Macroeconomic Variables, and Bagging
AbstractIn this paper we propose a smooth transition tree model for both the conditional mean and variance of the short-term interest rate process. The estimation of such models is addressed and the asymptotic properties of the quasi-maximum likelihood estimator are derived. Model specification is also discussed. When the model is applied to the US short-term interest rate we find (1) leading indicators for inflation and real activity are the most relevant predictors in characterizing the multiple regimes’ structure; (2) the optimal model has three limiting regimes. Moreover, we provide empirical evidence of the power of the model in forecasting the first two conditional moments when it is used in connection with bootstrap aggregation (bagging).
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Bibliographic InfoPaper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 570.
Date of creation: Mar 2010
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short-term interest rate; regression tree; smooth transition; conditional variance; bagging; asymptotic theory;
Other versions of this item:
- Francesco Audrino & Marcelo C. Medeiros, 2011. "Modeling and forecasting short‐term interest rates: The benefits of smooth regimes, macroeconomic variables, and bagging," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 999-1022, 09.
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- Erik Hillebrand & Tae-Hwy Lee & Marcelo Cunha Medeiros, 2012.
"Let´s do it again: bagging equity premium predictors,"
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604, Department of Economics PUC-Rio (Brazil).
- Eric Hillebrand & Tae-Hwy Lee & Marcelo C. Medeiros, 2012. "Let's Do It Again: Bagging Equity Premium Predictors," CREATES Research Papers 2012-41, School of Economics and Management, University of Aarhus.
- Huiyu Huang & Tae-Hwy Lee, 2013. "Forecasting Value-at-Risk Using High-Frequency Information," Econometrics, MDPI, Open Access Journal, vol. 1(1), pages 127-140, June.
- Francesco Audrino, 2012. "What Drives Short Rate Dynamics? A Functional Gradient Descent Approach," Computational Economics, Society for Computational Economics, vol. 39(3), pages 315-335, March.
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