What Central Bankers Need to Know about Forecasting Oil Prices

By Christiane Baumeister, Lutz Kilian. 2014

167 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/b26c58fcd25d4433a5577c8f68eb76e5c6ccd680

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Citations in the corpus

Forecasting oil price realized volatility using information channels from other asset classes (2017, background): “See, for example, Abramson and Finizza (1991), Huntington (1994), Alquist et al. (2013), Baumeister and Kilian (2102, 2014, 2015). for instance, Hansen and Lunde, 2005; Engle and Sun, 2007; Tay et al., 2009).”
Asymmetries in the response of economic activity to oil price increases and decreases (2015, unknown): “and the last transformation is the net oil price increase over the previous 36-month maximum (Hamilton, 2003) (36)  = max {0  −max {−1  −36}}  (3)”
HOW DOES STOCK MARKET VOLATILITY REACT TO OIL PRICE SHOCKS? (2017, unknown): “However, as pointed out by Baumeister and Kilian (2014), macroeconomic models used in central banks are often specified at quarterly frequency.”
Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence (2018, background): “See, for instance, Baumeister and Kilian (2015, 2014), Baumeister et al .”
Do High-Frequency Financial Data Help Forecast Oil Prices? The Midas Touch at Work (2015, background): “Doing so would raise additional complications discussed in Baumeister and Kilian (2013a).”
Forecasting oil and stock returns with a Qual VAR using over 150 years of data (2017, background): “…too plays a crucial role in predicting both in and out-of-sample movements in oil (see for example, Kilian (2009), Kilian and Vigfusson (2013), Baumeister and Kilian (2014, 2015) and Baumeister et al., (2015)) and stock prices (see for example, Rapach, Wohar and Rangvid (2005), Goyal and…”
Energy Markets and Global Economic Conditions (2020, background): “There is ample evidence that forecast combinations work well for oil price forecasting and help improve forecast accuarcy especially at longer horizons (see, e.g., Baumeister and Kilian, 2014a, 2015; Baumeister et al., 2014; Funk, 2018; Garrett et al., 2019; Manescu and van Robays, 2016).”