This article questions the slope homogeneity in a gravity equation and proposes a partially heterogeneous framework for its estimation using panel data. We suggest to employ K-mean clustering to group countries according to the gravity equation variables. Further, the gravity model is estimated...

It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact we extend the Dynamic Conditional Correlation (DCC) model by allowing for a clustering structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to...

Cluster-robust inference is widely used in modern empirical work in economics and many other disciplines. The key unit of observation is the cluster. We propose measures of "high-leverage" clusters and "influential" clusters for linear regression models. The measures of leverage and partial...

This paper introduces an estimator for a general class of models under rank deficiency arising from high dimensionality, multicollinearity, or both. Our approach obtains a projection matrix that projects a high-dimensional (potentially growing p n) parameter vector into a reduced consistently...

Demonstration of nonlinear nonparametric regression technique using R-package "NNS" and comparison to kernel based regression methods in goodness of fit, partial derivative estimation, and out-of-sample extrapolation

This paper introduces a method which permits valid inference given a finite number of heterogeneous, correlated clusters. Many inference methods assume clusters are asymptotically independent or model dependence across clusters as a function of a distance metric. With panel data, these...

In linear regression analysis, the estimator of the variance of the estimator of the regression coefficients should take into account the clustered nature of the data, if present, since using the standard textbook formula will in that case lead to a severe downward bias in the standard errors....

We provide a complete asymptotic distribution theory for clustered data with a large number of groups, generalizing the classic laws of large numbers, uniform laws, central limit theory, and clustered covariance matrix estimation. Our theory allows for clustered observations with heterogeneous...