This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. We introduce two new stacking approaches for DDML: short-stacking exploits the cross-fitting step of DDML to...

This paper provides new estimates of a time?varying NAIRU for Germany taking account of the structural break caused by German unification based on the Kalman Filter and on a partially linear model as two alternatives. Estimating a standard Phillips curve, the sum of coefficients associated with...

We consider the semiparametric regression X t +(Z) where and (r and function, and where the variables (X, Z) are endogeneous. We propose necessary and sufficient conditions for the identification of the parameters in the presence of instrumental variables. We also focus on the estimation of . An...

We estimate income elasticities for a variety of macro- and micro-nutrients using a sample of poor rural households in Mexico. The nutrient-income elasticity is estimated using a linear regression model controlling both for the clustered nature of our data and for the bias due to measurement...

There are theoretical foundations which allow hypothesizing on a positive association of life expectancy or retirement age with income. If both cannot be falsified, the relationship of income and the internal rate of return of a public pension system is not straight forward. By application of a...

We consider the problem of estimating a varying coefficient regression model when regressors include a time trend. We show that the commonly used local constant kernel estimation method leads to an inconsistent estimation result, while a local polynomial estimator yields a consistent estimation...

In the context of a partially linear regression model, shrinkage semiparametric estimation is considered based on the Stein-rule. In this framework, the coefficient vector is partitioned into two sub-vectors: the first sub-vector gives the coefficients of interest, i.e., main effects (for...

Partially linear errors-in-function models were proposed by Liang (2000), but their inferences have not been systematically studied. This article proposes an empirical likelihood method to construct confidence regions of the parametric components. Under mild regularity conditions, the...