We employ parametric and non-parametric cointegration to investigate the extent of integration between African stock markets and the rest of the world. Long-run correlation estimates imply very low association between the two. The two distinct cointegration approaches confirm the latter through recursive estimation. The implication is that global market movements may have little impact on Afric...

We present cointegration analysis as a method to infer the network structure of a linearly phase coupled oscillating system. By defining a class of oscillating systems with interacting phases, we derive a data generating process where we can specify the coupling structure of a network that resembles biological processes. In particular we study a network of Winfree oscillators, for which we pres...

This paper introduces a Bayesian Markov regime-switching model that allows the cointegration relationship between two time series to be switched on and off over time. Unlike classical approaches for testing and modeling cointegration, the Bayesian Markov switching method allows for estimation of the regime-specific model parameters via Markov Chain Monte Carlo and generates more reliable estima...

A survey is given of some results obtained for the cointegrated VAR. The Granger representation theorem is discussed and the notions of cointegration and common trends Ž . are defined. The statistical model for cointegrated I 1 variables is defined, and it is shown how hypotheses on the cointegrating relations can be estimated under suitable identification conditions. The asymptotic theory is b...

Time series cointegration tests, even in the presence of large sample sizes, often yield conflicting conclusions (“mixed signals”) as measured by, inter alia, a low correlation of empirical p-values [see Gregory et al., 2004, Journal of Applied Econometrics]. Using their methodology, we present evidence suggesting that the problem of mixed signals persists for popular panel cointegration tests....

This paper analyses the long-memory properties of US and European stock indices, as well as their linkages, using fractional integration and fractional cointegration techniques. These methods are more general and have higher power than the standard ones usually employed in the literature. The empirical evidence based on them suggests the presence of unit roots in both the S&P 500 Index and the ...

This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and cointegration spaces. Furthermore, the parameters which characterize short-run dynamics and deterministic c...

A Bayesian reference analysis of the cointegrated vector autoregression is presented based on a new prior distribution. Among other properties, it is shown that this prior distribution distributes its probability mass uniformly over all cointegration spaces for a given cointegration rank and is invariant to the choice of normalizing variables for the cointegration vectors. Several methods for c...

Testing for the equality of integration orders is an important topic in time series analysis because it constitutes an essential step in testing for (fractional) cointegration in the bivariate case. For the multivariate case, there are several versions of cointegration, and the version given in Robinson and Yajima (2002) has received much attention. In this definition, a time series vector is p...

According to several empirical studies, the linear present-value model fails to explain the behaviour of stock prices in the long run. We analyse the possible presence of threshold cointegration between real stock prices and dividends for the US market during the period from 1871:1 to 2004:6. According to our results, the null hypothesis of linear cointegration between stock prices and dividend...