Determinants of innovation performance in Spanish hospitality companies: analysis of the coexistence of innovation strategies

The present study attempts to determine the influence of different sources of technological knowledge on the innovation performance of hospitality companies in Spain (Europe) and analyze the degree of coexistence between innovation strategies in the companies. The paper considers a number of ways of incorporating technology and evaluates the impact of different innovation strategies on innovative performance in hospitality companies, in order to assess the importance of each in the Spanish hospitality industry. Employing data from the 2000 Survey on Technological Innovation, two databases were built with information on the Spanish hospitality industry. The first includes all hospitality companies and the second only companies engaged in innovation activities. With the second database, an analysis of the coexistence of innovation strategies is done by means of seven logit regressions. The results show that the innovation strategies that coexist most frequently are, first, the acquisition of external technology services and research and development cooperation, and second, the acquisition of machinery and non-material technologies. Additionally, the influence of different innovation strategies on companies' innovation performance is analyzed by means of a two-step logit model applied to both databases. The findings suggest that only the acquisition of machinery has a statistically significant impact on innovation performance. However, contrary to expectations, the influence appears to be negative.     

Asymptotic normality for estimator of conditional mode under left-truncated and dependent observations

In this paper, we establish asymptotic normality of a new kernel estimator of the conditional mode function introduced by Ould-Saïd and Tatachak (C R Acad Sci Paris Ser I 344:651–656, 2007) for the left-truncation model when the data exhibit some kind of dependence. It is assumed that the lifetime observations with multivariate covariates form a stationary α-mixing sequence.     

Predictive Systems: Living with Imperfect Predictors

We develop a framework for estimating expected returns—a  predictive system —that allows predictors to be imperfectly correlated with the conditional expected return. When predictors are imperfect, the estimated expected return depends on past returns in a manner that hinges on the correlation between unexpected returns and innovations in expected returns. We find empirically that prior beliefs about this correlation, which is most likely negative, substantially affect estimates of expected returns as well as various inferences about predictability, including assessments of a predictor's usefulness. Compared to standard predictive regressions, predictive systems deliver different expected returns with higher estimated precision.     

Estimating the long-run impact of forest fires on the eucalyptus timber supply in Galicia,Spain

Using annual data, we have estimated the timber harvest of eucalyptus in the region of Galicia (Spain). The explanatory variables considered were price, pulp exports, and salvage timber. The latter variable was used as a proxy for forest fires. The problems related to spurious regression were addressed by applying the bounds testing approach to cointegration, and confidence intervals were constructed using the bootstrap technique. The results indicate that pulp exports have a positive effect on the harvested timber volume. Moreover, we find that salvage timber positively affects the timber supply. This result indicates that there is no substitution between salvage timber and non-damaged timber. It also suggests that the natural expansion of the eucalyptus in Galicia compensates for the destruction caused by forest fires, avoiding supply shortages. On the other hand, and according to the economic law of supply, the timber price shows a positive effect.