Some remarks on the bias distribution analysis of discrete-time identification algorithms based on pseudo-linear regressions

In 1998, A. Karimi and I.D. Landau published in the journal "Systems and Control letters" an article entitled "Comparison of the closed-loop identification methods in terms of bias distribution". One of its main purposes was to provide a bias distribution analysis in the frequency domain of closed-loop output error identification algorithms that had been recently developed. The expressions provided in that paper are only valid for prediction error identification methods (PEM), not for pseudo-linear regression (PLR) ones, for which we give the correct frequency domain bias analysis, both in open- and closed-loop. Although PLR was initially (and is still) considered as an approximation of PEM, we show that it gives better results at high frequencies

How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observable covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design that are of practical importance, like sample size, the type of the outcome variable, and aspects of the selection process. We find that trimming individual observations with too much weight as well as the choice of tuning parameters is important for all estimators. The key conclusion from our simulations is that a particular radius matching estimator combined with regression performs best overall, in particular when robustness to misspecifications of the propensity score is considered an important property.propensity score matching, kernel matching, inverse probability weighting, selection on observables, empirical Monte Carlo study, finite sample properties

How to control for many covariates? Reliable estimators based on the propensity score

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observable covariates is required, like inverse pro¬bability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design that are of practical importance, like sample size, the type of the outcome variable, and aspects of the selection process. We find that trimming individual observations with too much weight as well as the choice of tuning parameters is important for all estimators. The key conclusion from our simulations is that a particular radius matching estimator combined with regression performs best overall, in particular when robustness to misspecifications of the propensity score is considered an important property.Propensity score matching, kernel matching, inverse probability weighting, selection on observables, empirical Monte Carlo study, finite sample properties

Boosting Ridge Regression

Ridge regression is a well established method to shrink regression parameters towards zero, thereby securing existence of estimates. The present paper investigates several approaches to combining ridge regression with boosting techniques. In the direct approach the ridge estimator is used to fit iteratively the current residuals yielding an alternative to the usual ridge estimator. In partial boosting only part of the regression parameters are reestimated within one step of the iterative procedure. The technique allows to distinguish between variables that are always included in the analysis and variables that are chosen only if relevant. The resulting procedure selects variables in a similar way as the Lasso, yielding a reduced set of influential variables. The suggested procedures are investigated within the classical framework of continuous response variables as well as in the case of generalized linear models. In a simulation study boosting procedures for different stopping criteria are investigated and the performance in terms of prediction and the identification of relevant variables is compared to several competitors as the Lasso and the more recently proposed elastic net

Incomplete panels and selection bias: A survey

Sampling;Panel Data

The wage effects of entering motherhood: a within-firm matching approach

We analyze the wage effects of employment breaks of women entering motherhood using a novel within-firm matching approach where mothers? wages upon return to the job are compared with those of their female colleagues within the same firm. Using an administrative German data set we investigate three different matching procedures based on information two years before birth: (1) exact matching on individual characteristics, (2) propensity score matching and (3) a combined procedure of exact and propensity score matching. Our results yield new insights into the nature of the wage penalty associated with motherhood, since we find first births to reduce women?s wages by 16 to 19 percent, regardless of the matching procedure applied. Neglecting the firm identifier and matching across all firms, however, yields a wage cut of 30 percent. Furthermore, we can show that the wage loss increases with the duration of the employment break. --wages,parental leave,matching