This paper considers the estimation of binary choice panel data models with discrete endogenous regressors. We present a switching probit model which accounts for selectivity bias as well as for other forms of time invariant unobserved heterogeneity. Individual effects are allowed to be...

The Dichotomous Choice Contingent Valuation Method can be used either in the single or double bound formulation. The former is easier to implement, while the latter is known to be more efficient. We analyse the bias of the ML estimates produced by either model, and the gain in efficiency...

We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, welfare and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic...

Survey under-coverage of top incomes leads to bias in survey-based estimates of overall income inequality. Using income tax record data in combination with survey data is a potential approach to address the problem; we consider here the UK's pioneering "SPI adjustment" method that implements...

DSGE models are typically estimated assuming the existence of certain primal shocks that drive macroeconomic fluctuations. We analyze the consequences of estimating shocks that are "non-existent" and propose a method to select the primal shocks driving macroeconomic uncertainty. Forcing these...

We propose a wild bootstrap procedure for linear regression models estimated by instrumental variables. Like other bootstrap procedures that we have proposed elsewhere, it uses efficient estimates of the reduced-form equation(s). Unlike them, it takes account of possible heteroskedasticity of...

This note gives a fairly complete statistical description of the Hodrick-Prescott Filter (1997) which has been proposed in the context of my seasonal adjustment method (Schlicht 1981, 1984). A statistics estimator for the smoothing parameter is proposed that is asymptotically equivalent to the...

This paper discusses the evaluation problem using observational data when the timing of treatment is an outcome of a stochastic process. We show that the duration framework in discrete time provides a fertile ground for effect evaluations. We suggest easy-to-use nonparametric survival function...

The conditions under which ordinary least squares (OLS) is an unbiased and consistent estimator of the linear probability model (LPM) are unlikely to hold in many instances. Yet the LPM still may be the correct model or a good approximation to the probability generating process. A sequential...