“…The majority of studies simply convert all the data at the lower available frequency by taking quarterly averages of monthly indicators, and the ragged-(or jagged-) edge nature of the data requires that missing monthly observations for the quarter to be forecast are predicted usually with univariate autoregressive models; on this, see McGuckin et al (2007). 3 Camacho and Perez-Quiros (2010), Camacho et al (2012), Ferrara et al (2010), Giannone et al (2009, Kuzin et al (2009) are notable exceptions, as they respectively use approximate Kalman filter models, Markov-switching dynamic factors, non parametric methods, mixedfrequency VARs, and MIDAS regressions of Clements and Galvão (2008).…”