This article proposes an alternative methodology to estimate impulse response functions without imposing parametric restrictions. The impulse responses are estimated by regressing the series of interest on estimated innovations, which are the residuals obtained from a prior-stage "long...

In least squares, least absolute deviations, and even generalized M-estimation, outlying observations sometimes strongly influence the estimation result, masking an important and interesting relationship existing in the majority of observations. The S-estimators are a class of estimators that...

Cleaning data or removing some data periods in least squares (LS) regression analysis is not unusual. This practice indicates that a researcher sometimes desires to estimate the parameter value, with which the regression function fits a large fraction of individuals or events in the population...

We conduct an empirical investigation of the impact of prenatal care-giver advice on alcohol consumption by pregnant women. In the design of the model and estimator, we pay particular attention to three aspects of the data. First, a large proportion of pregnant women do not drink at all. To...

Extending the L1-IV approach proposed by Sakata (1997, 2007), we develop a new method, named the $rho_{tau}$-IV estimation, to estimate structural equations based on the conditional quantile restriction imposed on the error terms. We study the asymptotic behavior of the proposed estimator and...

The authors show that quasimaximum likelihood (QML) estimators for conditional dispersion models can be severely affected by a small number of outliers such as market crashes and rallies, and they propose new estimation strategies (the two-stage Hampel estimators and two-stage S-estimators)...