Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance

By MarcoDel Negro, Raiden Hasegawa, Frank Schorfheide. 2016

150 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b39b001082f95b3f79be796db7bdfdbf6121933f

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. Florian Huber
2. Gary Koop
3. Stephanie Schmitt-Grohé
4. Frank Schorfheide
5. Morten O. Ravn
6. John M. Maheu
7. Anastasios Panagiotelis
8. Ruben Loaiza-Maya

In Finance

1. Stephanie Schmitt-Grohé
2. Ruben Loaiza-Maya
3. Andrew Gelman
4. Daniel P. Simpson
5. Gary Koop
6. Saroj Bhattarai
7. Gael M. Martin
8. Morten O. Ravn

In Management Science and Operations Research

1. Florian Huber
2. Gary Koop
3. John M. Maheu
4. Ruben Loaiza-Maya
5. Gael M. Martin
6. Anastasios Panagiotelis
7. Didier Nibbering
8. David T. Frazier

Citations in the corpus

• A MIDAS Approach to Modeling First and Second Moment Dynamics (2016, unknown): “Del Negro et al. (2014) develop a dynamic version of the prediction pool approach which they show works well for combinations of two models. Extending their approach to more than two models is computationally demanding so we stop short of implementing their approach empirically. However, it is worth”
• Bayesian forecasting in economics and finance: A modern review (2022, background): “The DPM specification has also been inserted into other popular models in finance, with: Jensen and Maheu (2014) adopting a DPM to jointly model the return and future log-volatility distribution; Delatola and Griffin (2013) capturing the so-called leverage effect; Auśın et al. (2014) applying a DPM”
• Macroeconomic Dynamics Near the ZLB: A Tale of Two Countries (2016, background): “In particular, following Eggertsson and Woodford (2003), many authors assume that an exogenous Markov-switching process pushes the economy to the ZLB.”
• Macroeconomic forecast accuracy in a data‐rich environment (2019, background): “9See Bates and Granger (1969), Hendry and Clements (2004), and Elliott et al. (2015) for theoretical and empirical demonstrations, and Boot and Nibbering (2019) for a theoretical derivation of expected gains of the CSR. Negro et al. (2016) and Elliott and Timmermann (2005) show that allowing weights”
• Using Stacking to Average Bayesian Predictive Distributions (with Discussion) (2017, methodology): “…of Bayesian density combination methods following the literature on combination of point forecasts (e.g. Hall and Mitchell, 2007; Geweke and Amisano, 2011; Billio et al., 2013; Aastveit et al., 2014; Kapetanios et al., 2015; Aastveit et al., 2018; Del Negro et al., 2016, and references therein).”