Robust Estimation and Inference
This line of
research involves M-Estimation, GMM, and GEL when tails are heavy, leading
to Gaussian asymptotics. Simultaneously I develop
robust methods of hypothesis testing for tests of functional form, white
noise, volatility spillover, etc. I also look at robust estimation of
coherent risk measures like the Expected Shortfall, robust estimation of Average
Treatment Effects when there may be limited overlap in the covariate
distribution, and robust variance targeting for heavy tailed GARCH models.
S. Chaudhuri, M. Aguilar, A. Prokhorov, E.
Mixed Frequency Data
I work on
problems of causation and indirect inference for multivariate mixed
frequency models. The deeper problems are identification of high frequency
relationships when some variables are not observe at a high frequency (e.g.
GDP), dimensionality problems that arise for stock or flow variables, and
asymptotic theory under minimal assumptions.
Co-authors: E. Ghysels,
Unified Inference for (Random
This line of work involves methods and theory
for unified estimation for AR and AR/RCA models. The error may be weakly
dependent and may have an infinite variance, and the parameter may be
random or not for a robust and unified test of a possibly stochastic unit
contributions here are asymptotic theory for tail index and tail dependence
estimation under possibly non-standard situations involving dependence and
heterogeneity. I develop a general notion of tail dependence, a
general weak limit theory for tail arrays, a functional limit theory for a
tail index estimator for dependent data, and consistent HAC estimators for
tail arrays allowing for non-parametric tail inference. I also work on
problems of tail index estimation for a latent or unobserved process where
a plug-in estimator is required and may be less than root-n convergent. I have extended my
work to first price auctions and to problems that arise with average