This paper consider penalized empirical loss minimization of convex loss functions with unknown non-linear target functions. Using the elastic net penalty we establish a finite sample oracle inequality which bounds the loss of our estimator from above with high probability. If the unknown target...

The zero inflated Poisson regression model is very common when analysing economic data that comes in the form of non-negative integers since it accounts for excess zeros and over-dispersion of the dependent variable. This model may be used in innovation analysis to see for example the impact on...

This paper provides a theoretical and empirical appraisal of the shocks-institutions controversy as an explanation of unemployment heterogeneity in OECD countries. Since the influential work of Blanchard and Wolfers (2000), many studies have tried to explain the differences in the OECD...

This paper adds to the already vast economic literature on the respective role of shocks and institutions in unemployment dynamics. Conclusions of existing studies widely diverge. Such divergences are mainly due to a weak theoretical framework underlying the models which have been estimated. We...

Partial mean processes with generated regressors arise in several important econometric problems, such as the distribution of potential outcomes with continuous treatments and the quantile structural function in a nonseparable triangular model.  This paper proposes a fully nonparametric...

Most of the estimators suggested for the estimation of spatial autoregressive models are generally inconsistent in the presence of an unknown form of heteroskedasticity in the disturbance term. The estimators formulated from the generalized method of moments (GMM) and the Bayesian Markov Chain...

In this study, I investigate the necessary condition for consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally...

We consider a spatial econometric model containing a spatial lag in the dependent variable and the disturbance term with an unknown form of heteroskedasticity in innovations. We first prove that the maximum likelihood (ML) estimator for spatial autoregressive models is generally inconsistent...

We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of interest is a known, deterministic, but kinked function of an observed assignment variable. This design arises in many institutional settings where a policy variable (such as weekly...

We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of interest is a known, deterministic, but kinked function of an observed assignment variable. This design arises in many institutional settings where a policy variable (such as weekly...