Accounting for Missing Values in Score-Driven Time-Varying Parameter Models - Andre Lucas; Anne Opschoor; Julia Schaumburg (Tinbergen Institute, 2016)
We show that two alternative perspectives on how to deal with missing data in the context of the score-driven time-varying parameter models of Creal, Koopman, Lucas (2013) and Harvey (2013) lead to precisely the same dynamic transition equations. As score-driven models encompass a wide variety of time-varying parameter models (including generalized autoregressive conditional volatility (GARCH) and duration (ACD) models), the results apply to a wide range of empirically relevant models as applied in economics and statistics.
Keywords: C52; C53; 330; generalized autoregressive score models; missing completely at random; Expectation-Maximization