Zobrazeno 1 - 10
of 141
pro vyhledávání: '"Nielsen, Morten Orregaard"'
For linear regression models with cross-section or panel data, it is natural to assume that the disturbances are clustered in two dimensions. However, the finite-sample properties of two-way cluster-robust tests and confidence intervals are often poo
Externí odkaz:
http://arxiv.org/abs/2406.08880
We study cluster-robust inference for binary response models. Inference based on the most commonly-used cluster-robust variance matrix estimator (CRVE) can be very unreliable. We study several alternatives. Conceptually the simplest of these, but als
Externí odkaz:
http://arxiv.org/abs/2406.00650
We study statistical inference on unit roots and cointegration for time series in a Hilbert space. We develop statistical inference on the number of common stochastic trends embedded in the time series, i.e., the dimension of the nonstationary subspa
Externí odkaz:
http://arxiv.org/abs/2312.00590
We provide computationally attractive methods to obtain jackknife-based cluster-robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. We also propose several new variants of the wild cluster bootstrap, whi
Externí odkaz:
http://arxiv.org/abs/2301.04527
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 in which a dataset could be clustered. We propose two tests for the c
Externí odkaz:
http://arxiv.org/abs/2301.04522
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 enormous literature, but rather a more introductory treatment suitabl
Externí odkaz:
http://arxiv.org/abs/2211.10235
It is well known that, under suitable regularity conditions, the normalized fractional process with fractional parameter $d$ converges weakly to fractional Brownian motion for $d>1/2$. We show that, for any non-negative integer $M$, derivatives of or
Externí odkaz:
http://arxiv.org/abs/2208.02516
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 obtained by proper implementations of the bootstrap. Specifically, we sho
Externí odkaz:
http://arxiv.org/abs/2208.02028
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 observation for such a model is the cluster. We therefore propose cluster
Externí odkaz:
http://arxiv.org/abs/2205.03288
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 methods in many empirically relevant situations have been developed. In th
Externí odkaz:
http://arxiv.org/abs/2205.03285