Forecasts of US Short-Term Interest Rates: A Flexible Forecast Combination Approach
By Massimo Guidolin, Allan G. Timmermann. 2005
110 citations recorded by the source corpus.
This paper develops a flexible approach to combine forecasts of future spot rates with forecasts from time-series models or macroeconomic variables. We find empirical evidence that accounting for both regimes in interest rate dynamics and combining forecasts from different models helps improve the out-of-sample forecasting performance for US short-term rates. Imposing restrictions from the expectations hypothesis on the forecasting model are found to help at long forecasting horizons.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/128e62b49db25a12798d1155bce03302c1af6504
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Fotios Petropoulos
- Konstantinos Nikolopoulos
- Rob J Hyndman
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Evangelos Spiliotis
- Spyros Makridakis
- Francesco Ravazzolo
In Finance
- Francesco Ravazzolo
- David F. Hendry
- David E. Rapach
- Michael P. Clements
- Jennifer L. Castle
- Feng Li
- Xiaoqian Wang
- Yanfei Kang
Citations in the corpus
- The Evolution of Forecast Density Combinations in Economics (2018, background): “Guidolin and Timmermann (2009) introduce Markov-switching weights, and Hoogerheide et al. (2010) propose robust timevarying weights and account for both model and parameter uncertainty in model averaging.”
- Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance (2016, unknown): “The workhorse DSGE models at several central banks was the model developed by Smets and Wouters (2007) (henceforth, SW), which has no endogenous credit frictions and uses no financial market data other than the fed funds rate as an observable.”
- Forecasting: theory and practice (2020, background): “In Guseo and Mortarino (2015) this assumption was weakened, introducing a dynamic carrying capacity approach (Guseo and Guidolin, 2009, 2011).”