Equi-spaced sampling is the norm in applied work using high-frequency data. Due to trade intermittency, however, this traditional sampling scheme amounts to implicit random sampling. Under implicit random sampling and on continuous trajectories, we quantify the asymptotic biases of even realized...

We propose an observation-driven time-varying SVAR model where, in agreement with the Lucas Critique, structural shocks drive both the evolution of the macro variables and the dynamics of the VAR parameters. Contrary to existing approaches where parameters follow a stochastic process with random...

We propose a methodology for filtering, smoothing and assessing parameter and filtering uncertainty in misspecified score-driven models. Our technique is based on a general representation of the well-known Kalman filter and smoother recursions for linear Gaussian models in terms of the score of...