Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes

By Guillaume Chevillon, David F. Hendry. 2005

137 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/3f73f6178654e0da99613660e4fc1da4d4cb49da

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

1. Todd E. Clark
2. Massimiliano Marcellino
3. Michael W. McCracken
4. J. De Gooijer
5. Pierre Guérin
6. Rob J Hyndman
7. Barbara Rossi
8. Ken West

In Management Science and Operations Research

1. Todd E. Clark
2. Michael W. McCracken
3. Massimiliano Marcellino
4. J. De Gooijer
5. Pierre Guérin
6. Rob J Hyndman
7. Andrew T. Foerster
8. Barbara Rossi

Citations in the corpus

• Direct multi-step estimation and forecasting (2006, unknown): “This result is specific to finite samples as Chevillon and Hendry (2005) showed.”
• 25 years of time series forecasting (2006, background): “The problem of incorporating external (prior) information in the univariate ARIMA forecasts have been considered by Cholette (1982), Guerrero (1991) and de Alba (1993).”
• 25 Years of IIF Time Series Forecasting: A Selective Review (2005, background): “The problem of incorporating external (prior) information in the univariate ARIMA forecasts have been considered by Cholette (1982), Guerrero (1991) and de Alba (1993).”
• Forecast Combination and Model Averaging Using Predictive Measures (2007, unknown): “See Chevillon and Hendry (2005) for an in-depth discussion of the relative merits of direct forecasts models like (34) and the more traditional dynamic model with forecasts based on the chain rule of forecasting. In essence we view (34) as the reduced form of a joint model for yt and xt. The obvious”
• Markov-Switching MIDAS Models (2013, background): “However, unlike iterated forecasts, direct forecasts require to reestimate the model when the forecasting horizon changes (see Chevillon and Hendry 2005; Marcellino, Stock, and Watson 2006, for a comparison of the relative merits of iterated and direct forecasts). The crucial difference between MIDA”
• Evaluating Direct Multistep Forecasts (2005, result): “As described in studies such as Clements and Hendry ( 1996 ), Schorfheide ( 2005 ), Chevillon and Hendry ( 2005 ), and Marcellino et al.”
• Bayesian Mixed Frequency VARs (2015, background): “Marcellino et al. (2006) and Chevillon and Hendry (2005) discuss the advantages of both types of forecasting, and De Mol et al. (2008) show Bayesian VARs can have good forecasting performance.”