Pooling of Forecasts

By David F. Hendry, Michael P. Clements. 2004

534 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7c4b0dc05ae10fce0f68ee84687d628cb156a4a1

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Citations in the corpus

Forecasting US Recessions: The Role of Sentiment (2014, unknown): “probit model can be straightforwardly estimated using maximum likelihood estimation, and misspeciÖcation robust standard errors can be obtained as in Kauppi and Saikkonen (2008). In Section 5:1, we provide additional evidence using a dynamic probit model to show that our results are robust to the in”
The Continuing Puzzle of Short Horizon Exchange Rate Forecasting (2008, background): “In the presence of structural breaks, pooling can be appropriate even if the CW and the ENCNEW are not statistically signi cant (and of course the bootstrapped TU and DMW are not statistically signi cant) (see Hendry and Clements, 2004).”
To Combine Forecasts or to Combine Information? (2010, methodology): “However, they compare CF with the best individual forecast but here we compare CF with forecast by the CI model (the DGP in Hendry and Clements, 2004).”
Economic Forecasting: Some Lessons from Recent Research (2003, unknown): “Makridakis and Hibon (2000) record the latest in a sequence of such competitions, discussed by Clements and Hendry (2001a).”
Forecasting the realized range-based volatility using dynamic model averaging approach (2017, result): “By contrast, combining forecasts with different degrees of adaptability can reduce the above issues and outperform forecasts from individual models (Hendry and Clements, 2004; Stock and Watson, 2004).”
Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2010, unknown): “5 In an environment relevant to equity premium forecasting, Hendry and Clements (2004) show that combining forecasts across individual models can lead to improved forecast accuracy when individual models provide only partial (perhaps overlapping) descriptions of the data-generating process and are s”
Forecast Combination and Encompassing (2009, background): “ Hendry and Clements (2004) and Timmermann (2006) discuss a number of other reasons that would justify pooling.”
Forecasting in dynamic factor models subject to structural instability (2009, background): “…limited research on the effect of structural instability on forecasting using factor models, it is plausible that factor-based forecasts might be robust to certain types of structural instability, for reasons akin to those discussed in Hendry and Clements (2002) in the context of forecast pooling.”
Some Theoretical Results on Forecast Combinations (2018, unknown): “…estimation error as the source of the problem include Clemen and Winkler (1986), which investigate parameter instability as the underlying motive for error, and also Hendry and Clements (2004) that considers discrete shift in the data generating process and forecasting models that are mis-specifed.”