Measuring globalisation: An evolutionary economic approach to tracking the evolution of international trade

Operationalising and measuring the concept of globalisation is important, as the extent to which the international economy is integrated has a direct impact on industrial dynamics, national trade policies and firm strategies. Using complex systems network analysis with longitudinal trade data from 1938 to 2003, this paper presents a new way to measure globalisation. It demonstrates that some important aspects of the international trade network have been remarkably stable over this period. However, several network measures have changed substantially over the same time frame. Taken together, these analyses provide a novel measure of globalisation.

Outbound Travel Depends on Business Returns: Australian Evidence

In an earlier note, Collins and Tisdell (2002b) explored the possibility of a long-run relationship between Australian business returns and international business travel. Using annual data they found that such a relationship exists. The purpose of this study is to further examine this relationship using quarterly data for the time frame 1974:1 to 1999:4. In addition, previous studies on international business travel have offered some but not strong evidence for the existence of a positive relationship between the level of international business travel and real GDP of the origin country. This study suggests that the aggregate return on business investments is a better predictor of international business travel than GDP. The Engle-Granger and Johansen's maximum-likelihood cointegration procedures are used to show a long-term relationship exists between Australian outbound business travel and Australian business returns, but not with Real Australian GDP. Reasons for this relationship are discussed.

Zero-non-zero patterned vector error correction modeling for I(2) cointegrated time series with applications in testing PPP and stock market relationships

Vector error-correction models (VECMs) have become increasingly important in their application to financial markets. Standard full-order VECM models assume non-zero entries in all their coefficient matrices. However, applications of VECM models to financial market data have revealed that zero entries are often a necessary part of efficient modelling. In such cases, the use of full-order VECM models may lead to incorrect inferences. Specifically, if indirect causality or Granger non-causality exists among the variables, the use of over-parameterised full-order VECM models may weaken the power of statistical inference. In this paper, it is argued that the zero–non-zero (ZNZ) patterned VECM is a more straightforward and effective means of testing for both indirect causality and Granger non-causality. For a ZNZ patterned VECM framework for time series of integrated order two, we provide a new algorithm to select cointegrating and loading vectors that can contain zero entries. Two case studies are used to demonstrate the usefulness of the algorithm in tests of purchasing power parity and a three-variable system involving the stock market.