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This chapter aims at providing an understanding of the research and devlopment (R&D) process in the pharmaceutical industry, by exploring the methodological challenges and approaches in the assessment of the determinants of innovation in the pharmaceutical industry. It (i) discusses possible methodological approaches to model occurrence of events; (ii) describes in detail competing risks duration models as the best methodological option in light of the nature of pharmaceutical R&D processes and data; (iii) concludes with an estimation strategy and overview of potential covariates that have been found to correlate with the likelihood of failure of R&D pharmaceutical projects.

Purpose – Resource-based literature argues that firms’ export performances are influenced by a proper combination of their own resources and capabilities, allowing for sustainable competitive advantages. Because export activities are usually based on relationships between firms and foreign intermediaries, the adequate management of inter-organisational activities should also be analysed as a key determinant of export performance.

Originality – Our research adds to the existing literature by examining the role that management control systems (MCS) play in exploiting firms’ exporting capabilities and resources to optimise export performance.

Methodology – Utilising empirical data from Spanish exporters, an initial analysis of export efficiency was performed based on DEA and segmentation techniques. From efficiency scores, we tested causal relationships between MCS design and use and the optimisation of resources and capabilities when performing export activities.

Findings and practical implications – The main conclusions are that any type of control system was found to have a positive influence in export performance, but only social control establishes a difference in terms of efficiency. The results show that strong social controls increase firms’ efficiencies when managing export channels, allowing firms to achieve outputs superior to competitors with similar resources and capabilities. In addition, an efficiency ‘lifecycle’ was identified for relationships between exporters and intermediaries. A pro-cyclical effect was found among MCS design, use, and export efficiency; intense MCS were established in the first stages of a relationship but were relaxed after a certain level of efficiency was achieved, leading to a reduction of efficiency in the long term that compromised the continuity of relationships.

Anchoring vignettes have become a popular method to adjust self-assessed data for systematic differences in reporting behaviour to aid comparability, for example, of cross-country analyses. The method relies on the two fundamental assumptions of response consistency and vignette equivalence. Evidence on the validity of these assumptions is equivocal. This chapter considers the utility of the vignette approach by considering how successful the method is in moving self-assessed reports of health mobility towards objective counterparts. We draw on data from the Survey of Health, Ageing and Retirement in Europe (SHARE) and undertake pairwise country comparisons of cumulative distributions of self-reports, their objective counterparts and vignette adjusted reports. Comparison of distributions is based on tests for stochastic dominance. Multiple cross-country comparisons are undertaken to assess the consistency of results across contexts and settings. Both non-parametric and parametric approaches to vignette adjustment are considered. In general, we find the anchoring vignette methodology poorly reconciles self-reported data with objective counterparts.

This paper compares the predictive power of linear econometric and non-linear computational models for forecasting the inflation rate in the European Monetary Union (EMU). Various models of both types are developed using different monetary and real activity indicators. They are compared according to a battery of parametric and non-parametric test statistics to measure their performance in one- and four-step ahead forecasts of quarterly data. Using genetic-neural fuzzy systems we find the computational approach superior to some degree and show how to combine both techniques successfully.

In this paper we investigate the joint conditional distribution of health (life expectancy) and income growth, and its evolution over time. The conditional distributions of these two variables are obtained by applying non-parametric methods to a bivariate non-parametric regression system of equations. Analyzing the distributions of the non-parametric fitted values from these models we find strong evidence of movement over time and strong evidence of first-order stochastic dominance of the earlier years over the later ones. We also find strong evidence of second-order stochastic dominance by non-OECD countries over OECD countries in each period. Our results complement the findings of Wu, Savvides and Stengos (2008) who explored the unconditional behaviour of these joint distributions over time.

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study, GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk, despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.

This chapter investigates the determinants of the volatility of spread in the over-the-counter foreign exchange market and examines whether the relationships differ in the crisis periods. We compute the measures for the volatility of liquidity by using bid-ask spread data sampled at a high frequency of five minutes. By examining 11 currencies over a 13-year sample period, we utilize a balanced dynamic panel regression to investigate whether the risk associated with the currencies quoted or trading activity affects the variability of liquidity provision in the FX market and examine whether the crisis periods have any effect. We find that both the level of spread and volatility of spread increases during the crisis periods for the currencies of emerging countries. In addition, we find increases in risks associated with the currencies proxied by realized volatility during the crisis periods. We also show risks associated with the currency are the major determinants of the variability of liquidity and that these relationships strengthen during periods of uncertainty. First, we develop measures to capture the variability of liquidity. Our measures to capture the variability of liquidity are non-parametric and model-free variable. Second, we contribute to the debate of whether variability of liquidity is adverse to market participants by examining what drives the variability of liquidity. Finally, we analyze seven crisis periods, allowing us to document the effect of the crises on determinants of variability of liquidity over time.