Associate Professor - Department of Economics
Grønneberg, Steffen & Foldnes, Njål (2019)
A problem with discretizing Vale-Maurelli in simulation studies
Psychometrika, 84(2), s. 554- 561. Doi: 10.1007/s11336-019-09663-8
Previous influential simulation studies investigate the effect of underlying non-normality in ordinal data using the Vale–Maurelli (VM) simulation method. We show that discretized data stemming from the VM method with a prescribed target covariance matrix are usually numerically equal to data stemming from discretizing a multivariate normal vector. This normal vector has, however, a different covariance matrix than the target. It follows that these simulation studies have in fact studied data stemming from normal data with a possibly misspecified covariance structure. This observation affects the interpretation of previous simulation studies.
Grønneberg, Steffen & Holcblat, Benjamin (2019)
On partial-sum processes of ARMAX residuals
Annals of Statistics
Grønneberg, Steffen & Foldnes, Njål (2018)
Testing Model Fit by Bootstrap Selection
Structural Equation Modeling Doi: 10.1080/10705511.2018.1503543
Foldnes, Njål & Grønneberg, Steffen (2018)
Approximating Test Statistics Using Eigenvalue Block Averaging
We introduce and evaluate a new class of approximations to common test statistics in structural equation modeling. Such test statistics asymptotically follow the distribution of a weighted sum of i.i.d. chi-square variates, where the weights are eigenvalues of a certain matrix. The proposed eigenvalue block averaging (EBA) method involves creating blocks of these eigenvalues and replacing them within each block with the block average. The Satorra–Bentler scaling procedure is a special case of this framework, using one single block. The proposed procedure applies also to difference testing among nested models. We investigate the EBA procedure both theoretically in the asymptotic case, and with simulation studies for the finite-sample case, under both maximum likelihood and diagonally weighted least squares estimation. Comparison is made with 3 established approximations: Satorra–Bentler, the scaled and shifted, and the scaled F tests.
Foldnes, Njål & Grønneberg, Steffen (2017)
The asymptotic covariance matrix and its use in simulation studies
Grønneberg, Steffen & Foldnes, Njål (2017)
Covariance Model Simulation Using Regular Vines
Psychometrika, 82(4), s. 1035- 1051. Doi: 10.1007/s11336-017-9569-6
We propose a new and flexible simulation method for non-normal data with user-specified marginal distributions, covariance matrix and certain bivariate dependencies. The VITA (VIne To Anything) method is based on regular vines and generalizes the NORTA (NORmal To Anything) method. Fundamental theoretical properties of the VITA method are deduced. Two illustrations demonstrate the flexibility and usefulness of VITA in the context of structural equation models. R code for the implementation is provided.
Sucarrat, Genaro; Grønneberg, Steffen & Escribano, Alvaro (2015)
Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown
Computational Statistics & Data Analysis, 100, s. 582- 594. Doi: 10.1016/j.csda.2015.12.005
Foldnes, Njål & Grønneberg, Steffen (2015)
How general is the Vale-Maurelli simulation approach?
Psychometrika, 80(4), s. 1066- 1083. Doi: 10.1007/s11336-014-9414-0
Grønneberg, Steffen & Hjort, Nils Lid (2014)
The Copula Information Criteria
Grønneberg, Steffen (2011)
The Copula Information Criterion and Its Implications for the Maximum Pseudo-Likelihood Estimator
Kurowicka, Dorota & Joe, Harry (red.). Dependence modeling: vine copula handbook
Grønneberg, Steffen & Hjort, Nils Lid (2011)
On the errors committed by sequences of estimator functionals
Mathematical Methods of Statistics, 20(4), s. 327- 346. Doi: 10.3103/S106653071104003X
Foldnes, Njål; Grønneberg, Steffen & Hermansen, Gudmund Horn (2018)
Statistikk og Dataanalyse
[Textbook]. Cappelen Damm Akademisk.
|2011||University of Oslo, Institute of Mathematics||Ph.D.|
|2011 - Present||BI Norwegian Business School||Associate professor|