Regression Models with Mixed Sampling Frequencies
By E. Andreou, Eric Ghysels, Andros Kourtellos. 2010
312 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/2dbcff494d2564f2bf4a0397dd754eb5d586b9ba
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
- Christiane Baumeister
- Lutz Kilian
- Pierre Guérin
- Eric Ghysels
- T. Woldehanna
- John Hoddinott
- Stefan Dercon
- Bjørn Eraker
In Finance
- Eric Ghysels
- Xilong Chen
- Bjørn Eraker
- Pierre Guérin
- Lutz Kilian
- Christiane Baumeister
- Michael T. Owyang
- Michelle T. Armesto
Citations in the corpus
- Forecasting with Mixed Frequencies (2010, background): “Andreou, Ghysels, and Kourtellos (2010a) found that incorporating daily factors (obtained from using financial data in a dynamic factor model) improved the forecasting ability of their MIDAS model for some horizons.”
- Can the COVID-19 Pandemic and Oil Prices Drive the US Partisan Conflict Index? (2020, unknown): “The MIDAS regression relies on a flexible aggregation function with minimal restrictions, allowing a data-generated weighting scheme (Andreou et al., 2010).”
- Macroeconomics and the Reality of Mixed Frequency Data (2016, methodology): “which is the ADL MIDAS regression model discussed in Andreou et al. (2010). There are various parsimonious parameterizations suggested for such regressions, see e.”
- Do High-Frequency Financial Data Help Forecast Oil Prices? The Midas Touch at Work (2015, methodology): “The original MIDAS model requires nonlinear least squares estimation (see Andreou, Ghysels, and Kourtellos 2010).”
- News - Good or Bad - and its Impact on Volatility Predictions over Multiple Horizons (2011, background): “8 This is a generic issue pertaining to MIDAS regressions and temporal aggregation, see Andreou, Ghysels, and Kourtellos (2009) and Ghysels (2009) for further discussion.”
- Growth and Chronic Poverty: Evidence from Rural Communities in Ethiopia (2012, background): “As noted by Andreou et al. (2010), this can lead, under certain conditions, to biased and inconsistent parameter estimates.6 However, if we restrict ourselves to using data collected in 1994, 1999, 2004 and 2009, we have four evenly spaced rounds, which allows us to circumvent this problem.”
- Bayesian Mixed Frequency VARs (2015, methodology): “Other papers that use bridging type models include Baffigi et al. (2004), and those that use bridging with factors, such as Giannone et al. (2008) and Angelini et al.”