Regime switching with structural breaks in output convergence

Abstract

In this paper, we examine empirically GDP per capita convergence using an approach that explicitly allows for regime switching in the long memory parameter d within the context of a Markov Switching (MS)-ARFIMA framework. As existing methods used in the estimation of standard MS models, such as the EM algorithm are no longer appropriate, we will make use of the Viterbi algorithm to estimate the long memory MS model used by Tsay and Hardle (Tsay, W.-J., and W. K. Hardle. 2009. A Generalized Arfima Process with Markov-Switching Fractional Differencing Parameter. Journal of Statistical Computation and Simulation 79: 731-745.). We will classify the output gap series into two regimes, a high d and a low d regime, where a high d close to unity would imply persistence and lack of convergence. By examining the path of d parameter over time which enables us to observe non-convergent behavior in more detail, we find that converging behavior is diminishing over time and divergence is the dominant force.