We propose a method for forecasting individual outcomes and estimating random effects in linear panel data models and value-added models when the panel has a short time dimension. The method is robust, trivial to implement and requires minimal assumptions. The idea is to take a weighted average...

In this paper, we develop a novel high-dimensional coefficient estimation procedure based on high-frequency data. Unlike usual high-dimensional regression procedure such as LASSO, we additionally handle the heavy-tailedness of high-frequency observations as well as time variations of coefficient...

In this paper, we develop a robust non-parametric realized integrated beta estimator using high-frequency financial data contaminated by microstructure noises, which is robust to the stylized features, such as the time-varying beta and the dependence structure of microstructure noises. With this...

We develop a normative theory for constructing mean-variance portfolios robust to model misspecification. We identify two inefficient portfolios---an "alpha'' portfolio, representing latent asset demand, that depends only on pricing errors and a "beta'' portfolio that depends on factor risk...

Most literatures about robust optimal reinsurance-investment problem aim to maximum the value function under the worst-case scenario, but some insurers are optimistic, so it is more reasonable to consider a more smooth criterion named $\alpha$-maxmin criterion which seek a balance between the...

Central banks wish to avoid self-fulfilling fluctuations. Monetary rules with a unit response to real rates achieve this under the weakest possible assumptions about the behaviour of households and firms. They are robust to household heterogeneity, hand-to-mouth consumers, non-rational...