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The authors propose the information matrix test to assess the constancy of mean and variance parameters in vector autoregressions (VAR). They additively decompose it into several orthogonal components: conditional heteroskedasticity and asymmetry of the innovations, and their unconditional skewness and kurtosis. Their Monte Carlo simulations explore both its finite size properties and its power against i.i.d. coefficients, persistent but stationary ones, and regime switching. Their procedures detect variation in the autoregressive coefficients and residual covariance matrix of a VAR for the US GDP growth rate and the statistical discrepancy, but they fail to detect any covariation between those two sets of coefficients.

The advent of the Internet has created the possibility for exporters to serve international markets using virtual export channels (VECs). This paper identifies the factors that influence success in using these new channels to export markets. The paper suggests that how well firms use the technology is more important than what they use it for. Investment and commitment to the Internet influence successful implementation. Moreover, firms with an existing export sales capability fare better in using VECs.

This paper is concerned with estimation of the parameters of a high-frequency VAR model using mixed-frequency data, both for the stock and for the flow case. Extended Yule–Walker estimators and (Gaussian) maximum likelihood type estimators based on the EM algorithm are considered. Properties of these estimators are derived, partly analytically and by simulations. Finally, the loss of information due to mixed-frequency data when compared to the high-frequency situation as well as the gain of information when using mixed-frequency data relative to low-frequency data is discussed.

The purpose of this paper is to present an example of how the synergy of different competences in students' teams, out‐of‐the‐box thinking style and various motivation factors in a culturally diverse learning environment is the foundation for knowledge construction, driven by the idea generation process and co‐creation – the so‐called Medici effect, one of the benchmarks for excellence in eLearning.

Developing this case study in the area of eLearning and Virtual Education Community (VEC) involves passing through various stages: from providing social presence and cooperation of students to co‐creation in the eLearning environment by contriving “e‐students” in the VEC whose profiles and learning processes are managed by pairs/teams of real students with different experiences and types of behaviour.

The paper presents outcomes of the research focused on the formation of the VEC for students who are motivated in sharing common passions and principles, in developing good communicator‐and‐listener techniques, in studying and creating situations of success.

In order to use and promote this pedagogical approach, enhancement of the desk‐top technology is needed in terms of a user‐friendly interface for creating a VEC where everyone, including people with special needs, feels comfortable, interested, and motivated to communicate, learn, construct, and share knowledge.

The original “4A” (attention, actualization, attraction, and action) pedagogical model applied in eLearning is aimed at ensuring students' social and cognitive presence through their e‐profiles for self‐assessment and positioning, adjusting learning trajectories and monitoring learning results, as well as assessing progress and final outcomes of learning.

Refers to previous research on the relationship between returns for different asset classes and on cointegration; and applies Johansen’s (1988) methodology to develop a prediction model. Uses 1978‐1995 data on five US asset classes (treasury bills, long‐term bonds, large capitalization common stocks, unsecuritized real estate and securitized real estate equity) to investigate cointegration between them. Shows that the index of unsecuritized real estate is positively related to treasury bills and negatively related to long‐term bonds and securitized real estate; and that returns for it can be forecast more accurately by using VECM models rather than unrestricted VAR models. Considers the implications for portfolio allocation, compares the results with other research fundings and calls for further research.

The purpose of this paper is to examine the performance of the vector error‐correction (VEC) econometric modelling technique in predicting short‐ to medium‐term construction manpower demand.

The VEC modelling technique is evaluated with two conventional forecasting methods: the Box‐Jenkins approach and the multiple regression analysis, based on the forecasting accuracy on construction manpower demand.

While the forecasting reliability of the VEC modelling technique is slightly inferior to the multiple log‐linear regression analysis in terms of forecasting accuracy, the error correction econometric modelling technique outperformed the Box‐Jenkins approach. The VEC and the multiple linear regression analysis in forecasting can better capture the causal relationship between the construction manpower demand and the associated factors.

Accurate predictions of the level of manpower demand are important for the formulation of successful policy to minimise possible future skill mismatch.

The accuracy of econometric modelling technique has not been evaluated empirically in construction manpower forecasting. This paper unveils the predictability of the prevailing manpower demand forecasting modelling techniques. Additionally, economic indicators that are significantly related to construction manpower demand are identified to facilitate human resource planning, and policy simulation and formulation in construction.

The purpose of this paper is to investigate firm strategies and the exertion of market power in the brewing sector in Brazil following a merger between the two largest brewers (Brahma and Antarctica) that created Ambev and given that the existing literature is inconclusive on this subject

In this study the authors apply cointegration analysis to price series of beer brands. The authors use the reduced form vector error correction (VEC) model to measure the price responses of beer brands in terms of direction, magnitude and speed. The authors use monthly retail prices for the primary brands of beer in the city of São Paulo, Brazil's largest consumer market. Specifically, the authors use two sets of retail prices, one from bars (the main point of beer sales, with roughly 50% of market share) and another from supermarkets. The series range from 1994 to 2014, depending on the brand.

This study indicates that Ambev's two major brands (Skol and Brahma) behave as market leaders, while its third brand (Antarctica) has been used to challenge the low-price competitor (Nova Schin). The authors also found evidence that the pricing policies of Brahma and Antarctica have changed toward cooperation following the creation of Ambev.

The main limitation of this article is that the authors only had access to retailer data. As the merger involved brewers, the authors would ideally use manufacturer beer prices in their econometric analysis. However, the consistency of our results suggests that retailers have been passively transmitting brand strategies launched at a manufacturer level.

As the dominant firm created following the merger of the two largest brewers appears to use one of its brand to restrict entry of competitors and the premium brands to enjoy high profits, consumers tend to be harmed by high beer prices and lack of options. Furthermore, small and medium-size companies cannot grow due to entry barriers created by the dominant firm.

This paper is the first to apply cointegration analysis to examine the effect of mergers on pricing strategies. The robustness of this study suggests that this approach could be used for antitrust agencies to monitor post-merger strategies.