Stationarity of a Markov-Switching GARCH Model

This article investigates some structural properties of theMarkov-switching GARCH process introduced by Haas, Mittnik,and Paolella. First, a sufficient and necessary condition forthe existence of the weakly stationary solution of the processis presented. The solution is weakly stationary, and the causalexpansion of the Markov-switching GARCH process is also established.Second, the general conditions for the existence of any integer-ordermoment of the square of the process are derived. The techniqueused in this article for the weak stationarity and the high-ordermoments of the process is different from that used by Haas,Mittnik, and Paolella and avoids the assumption that the processstarted in the infinite past with finite variance. Third, asufficient and necessary condition for the strict stationarityof the Markov-switching GARCH process with possibly infinitevariance is given. Finally, the strict stationarity of the so-calledintegrated Markov-switching GARCH process is also discussed.     

Provision of health care services in Austria

It is the aim of this paper to investigate three important variables of the health care system in Austria, that is the number of physicians working in the ambulatory sector, the number of medical services provided by the physicians and the related costs, by applying time series analysis to the quantities under consideration. The work analyzes stationarity, autocorrelation functions, presents unit root tests and calculates the Beveridge Nelson decomposition for an ARIMA(1,1,0)-model. The obtained findings are used to forecast future trends based on past values.The author is indebted to Ulrike Leopold, Klaus Neusser, Thomas Url and Peter Zweifel for many useful conversations and suggestions as well as to Keith Chester for English proofreading. The first version of the analysis presented was done as part of a project that was carried out at the Austrian Federal Institute of Public Health on behalf of the Austrian Government.     

Evidence of panel stationarity from Chinese provincial and regional income

The aim of this paper is to examine whether Chinese provincial and regional real GDP and per capita real GDP are panel stationary for the period 1952–2003. We allow for multiple structural breaks based on a technique developed by Carrion-i-Silvestre et al. [Carrion-i-Silvestre, J. L., Barrio-Castro, T, D., & Lopez-Bazo, E. (2005). Breaking the panels: An application to the GDP per capita. Econometrics Journal, 8, 159–175]. Allowing for at most five structural breaks, we find that for 67% of the provinces, per capita real GDP is stationary; while we only find stationarity of real GDP for 17% of the provinces. However, when we extend the analysis to panel data models, we find statistically strong evidence of panel stationarity of Chinese provincial and regional income.     

Stochastic convergence of per capita carbon dioxide emissions and multiple structural breaks in OECD countries

This research applies an innovative panel data stationarity testing procedure developed by Carrion-i-Silvestre et al. [Carrion-i-Silvestre, J.L., Barrio-Castro, T.D. and Lopez-Bazo, E., 2005. Breaking the panels: An application to the GDP per capita, Econometrics Journal 8, 159–175.], which has the advantage of recognizing multiple structural breaks and the presence of cross-section dependence in order to re-investigate the hypothesis that per capita carbon dioxide (CO₂) emissions stochastically converge for 21 OECD countries from 1950 to 2002. Remarkably, the evidence clearly indicates that the panel dataset of relative per capita CO₂ emissions is stationary after the structural breaks and cross-sectional dependence are introduced into the model. These findings offer strong policy implications for governments, regardless of whether they are in “convergent group” or “divergent group” countries. We also find that the structural breaks in the 1960s and over the 1970–1982 period are associated with time periods of fossil fuel becoming the main source of productivity, higher oil prices, and the development of nuclear power.