We develop new tail-trimmed M-estimation methods for heavy tailed Nonlinear AR-GARCH models. Tail-trimming allows both identification of the true parameter and asymptotic normality for nonlinear models with asymmetric errors. In heavy tailed cases the rate of convergence is infinitesimally close...

This paper presents a consistent GMM residuals-based test of functional form for time series models. By relating two moments we deliver a vector moment condition in which at least one element must be non-zero if the model is mis-specified. The test will never fail to detect mis-specification of...

We develop a robust least squares estimator for autoregressions with possibly heavy tailed errors. Robustness to heavy tails is ensured by negligibly trimming the squared error according to extreme values of the error and regressor. Tail-trimming ensures asymptotic normality and...

We study the probability tail properties of the Inverse Probability Weighting (IPW) estimators of the Average Treatment Effect T when there is limited overlap in the covariate distribution. Our main contribution is a new robust estimator that performs substantially better than existing IPW...

This paper presents a variety of tests of volatility spillover that are robust to heavy tails generated by large errors or GARCH-type feedback. The tests are couched in a general conditional heteroskedasticity framework with idiosyncratic shocks that are only required to have a finite variance...