7 citations · 7 across the 6 of their papers we have counts for
6 papers · 1 filter
Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference
James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
We provide computationally attractive methods to obtain jackknife-based cluster-robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. W…
Testing for the appropriate level of clustering in linear regression models
James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
The overwhelming majority of empirical research that uses cluster-robust inference assumes that the clustering structure is known, even though there are often several possible ways…
Fractional integration and cointegration
Javier Hualde, Morten Ørregaard Nielsen
In this chapter we present an overview of the main ideas and methods in the fractional integration and cointegration literature. We do not attempt to give a complete survey of this…
Bootstrap inference in the presence of bias
Giuseppe Cavaliere, Sílvia Gonçalves, Morten Ørregaard Nielsen +1
We consider bootstrap inference for estimators which are (asymptotically) biased. We show that, even when the bias term cannot be consistently estimated, valid inference can be obt…
Cluster-Robust Inference: A Guide to Empirical Practice
James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
Methods for cluster-robust inference are routinely used in economics and many other disciplines. However, it is only recently that theoretical foundations for the use of these meth…
Leverage, Influence, and the Jackknife in Clustered Regression Models: Reliable Inference Using summclust
James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
We introduce a new Stata package called summclust that summarizes the cluster structure of the dataset for linear regression models with clustered disturbances. The key unit of obs…