Selection of estimation window in the presence of breaks

By M. Hashem, Pesaran, Allan G. Timmermann, James Chu, D. Hendry, Cheng Hsiao, A. Pagan. 2007

449 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a9570ef0e1caea315293b71d98e7517050852233

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Citations in the corpus

Out-of-Sample Forecast Tests Robust to the Choice of Window Size (2011, unknown): “This article is closely related to the works by Pesaran and Timmermann ( 2007 ) and Clark and McCracken ( 2009 ), and more distantly related to Pesaran, Pettenuzzo, and Timmermann ( 2006 ) and Giacomini and Rossi ( 2010 ).”
Advances in Forecasting Under Instability (2012, unknown): “On the other hand, Timmermann (2006) and Pesaran and Timmermann (2007) nd that forecast combinations in models with 42 varying degrees of adaptability to structural breaks at unknown times are better than forecasts from individual models.”
Short-run price forecast performance of individual and composite models for 496 corn cash markets (2017, background): “Efforts in selecting the optimal window size include [16,32,55].”
Consistent Factor Estimation in Dynamic Factor Models with Structural Instability (2013, background): “Pursuing this calculation would be of independent interest and would also return the large-dimensional discussion here to the bias-variance tradeoffs associated with ignoring breaks tackled in a low-dimensional setting by Pesaran and Timmermann (2005, 2007).”
Averaging Forecasts from Vars with Uncertain Instabilities (2006, unknown): “Accordingly, Timmermann (2006) and Pesaran and Timmermann (2007) suggest that combinations of forecasts from models with varying degrees of adaptability to uncertain structural breaks will be more accurate than forecasts from individual models.”
What if the UK or Sweden had joined the Euro in 1999? An empirical evaluation using a global VAR (2007, background): “The diagonal elements of Ω̃H̄ , that is {Ωii}i=1, are given by (14), while the off-diagonal elements can be expressed as”
Instability of Return Prediction Models (2006, background): “For this case, Pesaran and Timmermann (2004) prove that it can be optimal to use pre-break data provided that the break is not very large (so the bias in the predictive return regression does not get too large).”
Forecasting the Equity Risk Premium: The Role of Technical Indicators (2014, unknown): “fund guru George Soros (2003) argues that positive feedback can actually alter firm fundamentals, thereby justifying to a certain extent the price trends.”
Adaptive models and heavy tails with an application to inflation forecasting (2017, background): “Pesaran and Pick (2011), Pesaran, Pick, and Pranovich (2013) and Pesaran and Timmermann (2007) focus on optimal weighting schemes in the presence of structural breaks.”
Forecast Combination Across Estimation Windows (2011, methodology): “As shown by Pesaran and Timmermann (2007), in such circumstances a larger estimation window might be more appropriate.”
Improving Forecast Accuracy by Combining Recursive and Rolling Forecasts (2008, unknown): “3 Finally, our work on the optimal choice of observation window extends recent work by Pesaran and Timmermann (2007).”
Elusive Return Predictability (2008, background): “…discrete breaks in real time and estimate the parameters of the forecasting models either on the post-break data alone (if the break is large or the most recent break happened a long time ago) or some combination that weights post-break data more than pre-break data (Pesaran and Timmermann (2007)).”
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark (2020, background): “Interestingly, as [83] argue in an econometric context, in the presence of structural breaks it may be advisable to combine forecasts obtained for calibration windows of different lengths.”