Humanities and Social Sciences

Central European Journal of Economic Modelling and Econometrics


Central European Journal of Economic Modelling and Econometrics | 2016 | No 4 |

Download PDF Download RIS Download Bibtex


Small sample properties of unrestricted and restricted canonical correlation estimators of cointegrating vectors for panel vector autoregressive process are considered when the cross-sectional dependencies occur in the process generating nonstationary panel data. It is shown that the unrestricted Box-Tiao estimator is slightly outperformed by the unrestricted Johansen estimator if the dynamic properties of the underlying process are correctly specified. The comparison of performance of the restricted canonical correlation estimator of cointegrating vectors for the panel VAR and for the classical VAR applied independently for each cross-section reveals that the latter performs better in small samples when the cross-sectional dependence is limited to the error terms correlations, even though it is inefficient in the limit, but it falls short in comparison to the former when there are cross-sectional dependencies in the short-run dynamics and/or in the long-run adjustments.

Go to article

Authors and Affiliations

Piotr Kębłowski
Download PDF Download RIS Download Bibtex


We apply Bayesian inference to estimate transformation matrix that converts vector of industry outputs from NACE Rev. 1.1 to NACE Rev. 2 classification. In formal terms, the studied issue is a representative of the class of matrix balancing (updating, disaggregation) problems, often arising in the field of multi-sector economic modelling. These problems are characterised by availability of only partial, limited data and a strong role for prior assumptions, and are typically solved using bi-proportional balancing or cross-entropy minimisation methods. Building on Bayesian highest posterior density formulation for a similarly structured case, we extend the model with specification of prior information based on Dirichlet distribution, as well as employ MCMC sampling. The model features a specific likelihood, representing accounting restrictions in the form of an underdetermined system of equations. The primary contribution, compared to the alternative, widespread approaches, is in providing a clear account of uncertainty.

Go to article

Authors and Affiliations

Jakub Boratyński
Download PDF Download RIS Download Bibtex


The first so-called hybrid MSV-MGARCH models were characterized by the conditional covariance matrix that was a product of a univariate latent process and a matrix with a simple MGARCH structure (Engle’s DCC or scalar BEKK). The aim was to parsimoniously describe volatility of a large group of assets. The proposed hybrid models, similarly as pure MSV specifications (and other models based on latent processes), required the Bayesian approach equipped with efficient MCMC simulation tools. The numerical effort has payed – the hybrid models seem particularly useful due to their good fit and ability to jointly cope with large portfolios. In particular, the simplest hybrid, now called the MSF-SBEKK model, has been successfully used in many applications. However, one latent process may be insufficient in the case of a highly heterogeneous portfolio. Thus, in this study we discuss a general hybrid MSV-MGARCH model structure, showing its basic characteristics that explain greater flexibility of such hybrid structure with respect to the corresponding MGARCH class. From the empirical perspective, we advocate the GMSF-SBEKK specification, which uses as many latent processes as there are relatively homogeneous groups of assets. We present full Bayesian inference for such models, with the use of an efficient MCMC simulation strategy. The approach is used to jointly model volatility on very different markets. Joint modelling is formally compared to individual modelling of volatility on each market.

Go to article

Authors and Affiliations

Jacek Osiewalski
Krzysztof Osiewalski

Editorial office

JACEK OSIEWALSKI, Cracow University of Economics, Poland
ALEKSANDER WELFE, University of Lodz, Poland


KATARZYNA BIEŃ-BARKOWSKA, SGH Warsaw School of Economics, Poland
MIKOŁAJ CZAJKOWSKI, University of Warsaw, Poland
JAKUB GROWIEC, SGH Warsaw School of Economics, Poland
MAREK GRUSZCZYŃSKI, SGH Warsaw School of Economics, Poland
BOGUMIŁ KAMIŃSKI, SGH Warsaw School of Economics, Poland
MARCIN KOLASA, SGH Warsaw School of Economics, Poland
ANNA PAJOR, Cracow University of Economics, Poland

Associate Editors
KARIM ABADIR, The American University in Cairo, Cairo, Egypt
ANINDYA BANERJEE, University of Birmingham, UK
STEPHEN HALL, University of Leicester, UK
GARY KOOP, University of Strathclyde, Glasgow, UK
MARK STEEL, University of Warwick, UK
MARTIN WAGNER, Technical University of Dortmund, Germany
JAN WERNER, University of Minnesota, USA
PETER WINKER, University of Giessen, Germany

Editorial Board

HERMAN van DIJK, Erasmus University Rotterdam and VU University Amsterdam, The Netherlands
LAWRENCE R. KLEIN, University of Pennsylvania, Benjamin Franklin Professor of Economics, USA
TIMO TERASVIRTA, University of Aarhus, Denmark
HELMUT LUETKEPOHL, Freie Universität Berlin, Germany

Publishing Editor

ANNA STASZEWSKA-BYSTROVA, University of Lodz, Poland

Editorial Assistant

AGNIESZKA RYGIEL, Cracow University of Economics, Poland


CEJEME Editorial Office - Ms. Karolina Jaszczyk, Polish Academy of Sciencies - Lodz Branch
Piotrkowska Str. 137/139, 90-434 Lodz, Poland

Instructions for authors

Submission Guidelines and Instructions for Authors of accepted papers please visit:

This page uses 'cookies'. Learn more