A new point estimator for the AR(1) coefficient in the linear regression model with arbitrary exogenous regressors and stationary AR(1) disturbances is developed. Its construction parallels that of the median--unbiased estimator, but uses the mode as a measure of central tendency. The mean--adjusted estimator is also considered, and saddlepoint approximations are used to lower the computational...

‎We extend the method of adaptive two-stage sequential sampling to‎‎include designs where there is more than one criteria is used in‎‎deciding on the allocation of additional sampling effort‎. ‎These‎‎criteria‎, ‎or conditions‎, ‎can be a measure of the target‎‎population‎, ‎or a measure of some related population‎. ‎We develop‎‎Murthy estimator for the design that is unbiased estimators for‎‎t...

The usual methods for analyzing case-cohort studies rely on sometimes not fully efficient weighted estimators. Multiple imputation might be a good alternative because it uses all the data available and approximates the maximum partial likelihood estimator. This method is based on the generation of several plausible complete data sets, taking into account uncertainty about missing values. When t...

This study proposes a new estimator for estimating a treatment effect in one particular fuzzy regression discontinuity (RD) setting, in which the treatment effect is homogeneous on the support of an assignment variable and the treatment assignment is exogenous conditional on that assignment variable. The estimator is constructed using orthogonality conditions and can be easily implemented by an...

In linear regression with heteroscedastic errors, the Generalized Least Squares (GLS) estimator is optimal, i.e., it is the Best Linear Unbiased Estimator (BLUE). The Ordinary Least Squares (OLS) estimator is suboptimal but still valid, i.e., unbiased and consistent. Halbert White, in his seminal paper (Econometrica, 1980) used the OLS residuals in order to obtain an estimate of the standard er...