This paper is concerned with parameter estimation and inference in a cointegrating regression, where as usual endogenous regressors as well as serially correlated errors are considered. We propose a simple, new estimation method based on an augmented partial sum (integration) transformation of...

We show how to apply the fully-modified estimation method in the integrated seemingly unrelation regressions model. Three different fully-modified estimators are studied and their asymptotic distributions are found.

It is shown that the maximum likelihood estimator of a local to unity parameter can be consistently estimated with panel data when the cross section observations are independent. Consistency applies when there are no deterministic trends or when there is a homogenous deterministic trend in the...

In the paper we consider the role of seasonal intercepts in seasonal cointegration analysis.

The classical definition of GARCH-type processes rely on strong assumptions on the first two conditional moments. The common practice in empirical studies, however, has been to test for GARCH by detecting serial correlations in the squared regression errors. This can be problematic since such...

This paper provides a textbook example of integration between commodity markets, and the subsequent price convergence or absence thereof. We analyze price relations between spot markets for natural gas in Europe. We apply time-varying coefficient estimation applying the Kalman filter, to test...

Purpose: The purpose of the paper was to estimate the interdependence between selected macroeconomic variables and non-performing loans in Ghana using a Bayesian Vector autoregressive approach. Design/methodology/approach: This paper used annual series from 2008-2017 which was interpolated into...

In this paper, we investigate the asymmetry in the tail dependence between USequity portfolios and the aggregate US market. Given the limited number of ob-servations in the tails of a joint distribution, standard non-parametric measures oftail dependence often have poor nite-sample properties....

High-dimensional regression problems which reveal dynamic behavior are typicallyanalyzed by time propagation of a few number of factors. The inference on thewhole system is then based on the low-dimensional time series analysis. Such highdimensional problems occur frequently in many different...