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Found 7 papers, 1 papers with code
Inference on common trends in functional time series
We study statistical inference on unit roots and cointegration for time series in a Hilbert space.
Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference
We provide computationally attractive methods to obtain jackknife-based cluster-robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares.
Testing for the appropriate level of clustering in linear regression models
We propose two tests for the correct level of clustering in regression models.
Fractional integration and cointegration
In this chapter we present an overview of the main ideas and methods in the fractional integration and cointegration literature.
Bootstrap inference in the presence of bias
We consider bootstrap inference for estimators which are (asymptotically) biased.
Leverage, Influence, and the Jackknife in Clustered Regression Models: Reliable Inference Using summclust
We introduce a new Stata package called summclust that summarizes the cluster structure of the dataset for linear regression models with clustered disturbances.
Cluster-Robust Inference: A Guide to Empirical Practice
However, it is only recently that theoretical foundations for the use of these methods in many empirically relevant situations have been developed.
