Real Time Forecasts of Inflation: The Role of Financial Variables

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A Survey of Econometric Methods for Mixed-Frequency Data (2013, background): “yTm+hm|Tm+w = μ̂+GF̂Tm+w (51) G = Σ̃yF (hm − w)× Σ̂−1 F , (52)”
Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials (2015, background): “However, when financial data come into play as in Andreou et al. (2010b) and Monteforte and Moretti (2010), we face more severe limits in the degrees of freedom and functional lag polynomials may be preferable.”
State Space Models and MIDAS Regressions (2013, methodology): “The above scheme, also used by Harvey (1989) and Nunes (2005), covers both stock and flow aggregation, and y t is a cumulator variable.”
Foreign direct investment and firm performance: an empirical analysis of Italian firms (2016, unknown): “924 Dependent variables are in logs. Percentage premia are computed by the exponential transformation of the estimated MNE coefficients. WLP: Wooldridge (2009); WLP-M: Wooldridge (2009) with omitted price bias correction; LP: Levinsohn and Petrin (2003); ACF: Ackerberg et al.”