Robust inference in time series analysis is concerned with developing statistical methods that remain valid under departures from standard model assumptions, such as the presence of heteroskedasticity ...

This paper investigates a selection of methods disentangling contributions from price jumps to realized variance. Flat prices (prices sampled consecutively in calendar time with the same value) and no ...

Under a linear regression model, the best linear unbiased estimator (BLUE) for a finite population total can be obtained. The problem studied here is that of estimating the variance for setting ...

This paper investigates robust optimization methods for mean-variance portfolio selection problems under the estimation risk in mean returns. We show that with an ellipsoidal uncertainty set based on ...

Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the ...