Testing convergence of Fiji's tourism markets

The convergence hypothesis for tourism markets is based on the tenet that when tourism markets are converging the difference between total international visitor arrivals to a country and international visitor arrivals from a particular country will be stationary. We argue that if this is true, then convergence can also be tested through examining whether total visitor arrivals and visitor arrivals from a particular market are cointegrated. We test the convergence hypothesis by examining visitor arrivals to Fiji from eight tourist source markets, using both unit root and cointegration tests. We find strong statistical evidence that Fiji's tourism markets converge.

A neural networks learning algorithm for minor component analysis and its convergence analysis

The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.