State Space Models and MIDAS Regressions

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Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials (2015, methodology): “For example, Clements and Galvão (2008, 2009) proposed to use MIDAS for forecasting quarterly GDP growth by using monthly business cycle indicators; see also Kuzin et al. (2011), Bai et al. (2013) and Marcellino and Schumacher (2010), among others.”
Macroeconomics and the Reality of Mixed Frequency Data (2016, background): “Bai et al. (2013) show that the above specification matches a steady state Kalman filter prediction equation obtained from a single factor state space model and provides a good approximation formanymore complex state spacemodel specifications.”
Bayesian Mixed Frequency VARs (2015, background): “…of stock returns over relative low frequencies (monthly or quarterly) from high frequency volatility estimates, Andreou et al. (2010) consider the importance of daily data for forecasting monthly or quarterly real data, and Bai et al. (2013) expand MIDAS to deal with state-space models.”
Real-Time Forecasting With a Mixed-Frequency VAR (2015, unknown): “Bai, Ghysels, and Wright (2013) examined the relationship between MIDAS regressions and state-space models applied to mixed-frequency data.”