Unit-Root Tests Are Useful for Selecting Forecasting Models
By Francis X. Diebold, Lutz Kilian. 1999
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- George Athanasopoulos
- Rob J Hyndman
- D. Wu
- Haiyan Song
- Allan G. Timmermann
- Konstantinos Nikolopoulos
- Fotios Petropoulos
- Michael P. Clements
Citations in the corpus
- The Tourism Forecasting Competition (2011, background): “Diebold and Kilian (2000) find strong evidence that unit root pretesting for selecting the level of differencing d improves the forecasting accuracy.”
- Forecasting with difference‐stationary and trend‐stationary models (2001, background): “To find some general rules that might guide model choice in practice, Diebold and Kilian (1999) study by Monte Carlo whether unit-root pre-testing produces more accurate forecasts.”
- Short-Term Estimates of Euro Area Real GDP by Means of Monthly Data (2003, background): “In contrast, and quite interestingly, λ is estimated to be larger than one for business 6 Diebold and Kilian (2000) argue that differencing in order to render the series stationary, according to unit root pre-testing, improves the forecasting performance.”
- Averaging estimators for autoregressions with a near unit root (2010, unknown): “All rights reserved.”
- Bayesian VARs : specification choices and forecast accuracy (2011, unknown): “Diebold and Kilian (2000) show that, for variables with unit roots, forecasting accuracy can be improved by differencing.”
- Forecasts of US Short-Term Interest Rates: A Flexible Forecast Combination Approach (2005, background): “See also the discussion in Diebold and Kilian (2000) of the importance for forecasting performance of correctly identifying the order of integration.”
- Bootstrap-After-Bootstrap Prediction Intervals for Autoregressive Models (2001, background): “The use of pre-test information related to the presence of unit roots and cointegrating restrictions can affect small-sample performance of prediction intervals (Diebold and Kilian 2000) and is a subject of future research.”
- What Do We Learn from the Price of Crude Oil Futures (2010, unknown): “The favorable forecasting performance of the no-change forecast also is consistent with the observed high persistence of the nominal spot price of oil (see, for example, Diebold and Kilian, 2000).”