Bayesian nonparametric models based on infinite mixtures of density kernels have been recently gaining in popularity due to their flexibility and feasibility of implementation even in complicated modeling scenarios. In economics, they have been particularly useful in estimating nonparametric...

Economists often use matched samples, especially when dealing with earnings data where a number of missing observations need to be imputed. In this paper, we demonstrate that the ordinary least squares estimator of the linear regression model using matched samples is inconsistent and has a...

This essay focuses on the genesis of ideas of nonlinearity, stochastics, and dynamics in economic thought as a series of intellectual advances that connected the linear static (quasi-dynamic) determinism of the 18th-19th centuries with the linear mechanistic systems with stochastic terms and the...

We propose a new sequential procedure for estimating a dynamic joint distribution of a group of assets. The procedure is motivated by the theory of composite likelihood and by the theory of copula functions. It recovers m-variate distributions by coupling univariate distributions with...

We estimate intergenerational income mobility in the USA and Sweden. To measure the degree to which income status is transmitted from one generation to another we propose a nonparametric estimator, which is particularly relevant for cross-country comparisons. Our approach allows...

Economists often use matched samples, especially when dealing with earnings data where a number of missing observations need to be imputed. In this paper, we demonstrate that the ordinary least squares estimator of the linear regression model using matched samples is inconsistent and has a...

We estimate intergenerational income mobility in the USA and Sweden. To measure the degree to which income status is transmitted from one generation to another we propose a nonparametric estimator, which is particularly relevant for cross-country comparisons. Our approach allows...

We propose a nonparametric measure of association between any number of random vectors that is based on the empirical copula process. The measure is insensitive to the dependence of components within vectors and only captures association between vectors as a whole. We calculate approximate...