Time-varying volatility is common in macroeconomic data and has been incorporated into macroeconomic models in recent work. Dynamic panel data models have become increasingly popular in macroeconomics to study common relationships across countries or regions. This paper estimates dynamic panel...

We consider likelihood inference and state estimation by means of importance sampling for state space models with a nonlinear non-Gaussian observation y ~ p(y lpha) and a linear Gaussian state alpha ~ p(alpha). The importance density is chosen to be the Laplace approximation of the smoothing...

This paper develops an unbiased Monte Carlo approximation to the transition density of a jump-diffusion process with state-dependent drift, volatility, jump intensity, and jump magnitude. The approximation is used to construct a likelihood estimator of the parameters of a jump-diffusion observed...

Average treatment effects estimands can present significant bias under the presence of outliers. Moreover, outliers can be particularly hard to detect, creating bias and inconsistency in the semi-parametric ATE estimads. In this paper, we use Monte Carlo simulations to demonstrate that...

We consider a non-parametric model for estimating the effect of a binary treatment on an outcome variable while adjusting for an observed covariate. A naive procedure consists in performing two separate non-parametric regression of the response on the covariate: one with the treated individuals...

The taxation of property based on market values requires frequent appraisals for a large number of properties. In light of the recent property tax reform discussion in Germany, it has been argued that a valuebased tax therefore cannot be implemented at a reasonable cost. In several other...

Outliers can be particularly hard to detect, creating bias and inconsistency in the semi-parametric estimates. In this paper, we use Monte Carlo simulations to demonstrate that semi-parametric methods, such as matching, are biased in the presence of outliers. Bad and good leverage point outliers...

This study proposes a Bayesian semiparametric binary response model using Markov chain Monte Carlo algorithms since this Bayesian algorithm works when the maximum likelihood estimation fails. Implementing graphic processing unit computing improves the computation time because of its efficiency...

Non-random item nonreponse makes identification if parameters problematic. Such nonresponse can occur with respect to both dependent and conditioning variables. A method often used to reduce nonresponse is that of adding unfolding brackets as follow up to open-ended questions. With these,...

Economic data are often contaminated by measurement errors and truncated by ranking. This paper shows that the classical measurement error model with independent and additive measurement errors is identified nonparametrically using only two order statistics of repeated measurements. The...