Innovation diffusion with generation substitution effects

Markets of high technology products and services, such as telecommunications, are described by fast technological changes and rapid generational substitutions. Since the conventional modeling approaches that are based on diffusion models do not usually incorporate this important aspect into their formulations, the accuracy of the provided forecasts is consequently affected. The work presented in this paper is concerned with the development of a methodology for describing innovation diffusion, in the context of generation substitution. For this purpose, a dynamic diffusion model is developed and evaluated, based on the assumption that the saturation level of the market does not remain constant throughout the diffusion process but is affected by the diffusion of its descendant generation, as soon as the latter is introduced into the market. In contradiction to the conventional diffusion models, which assume static saturation levels, the proposed approach incorporates the effects of generation substitution and develops a diffusion model with a dynamic ceiling. The importance of such an approach is especially significant for markets characterized by rapid technological and generational changes. Evaluation of the proposed methodology was performed over 2G and 3G historical data and for a number of European countries, providing quite accurate estimation and forecasting results, along with important information regarding the rate of generation substitution.     

The short-term impact of director trading in UK closed-end funds

Most closed-end funds are transparent entities that hold securities that are actively traded in liquid markets. In such a setting, the argument that director transactions mitigate information asymmetry has very limited applicability. Our results provide support for the theory of Barber and Odean [2008. “All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors.” Review of Financial Studies 21: 785–818]: retail investor decision-making is influenced by attention-grabbing events. Director purchases are one such attention-grabbing event and are associated with significant positive price returns – the magnitudes of which are linked to the size of the purchase, the size of the fund, and the investment mandate. Trading volumes increase at the time of the purchase but most of the initial price responses and trading volumes dissipate over the following 15 days.     

Economic efficiency of Greek retail SMEs in a period of high fluctuations in economic activity: a DEA approach

This study aims to analyse the economic efficiency of Greek small and medium retail enterprises before and after the crisis that started in 2008. Based on the Accounting Equation, we use Data Envelopment Analysis (DEA) to estimate variable returns of scale efficiency scores and to conclude on specific characteristics that efficient companies have, for example, on capital structure. Our results from the DEA application show a high degree of inefficiency. We found that SMEs on the islands are more efficient than those on the mainland and that SMEs in the cities are the least efficient. Size seems to be important, more so on the islands and on the mainland than in the cities. We conclude that companies should act more conservatively in terms of operating cost when the first signs of a recession appear. In addition, during a recession period, companies that have evidences that their operations will continue positively should strengthen their operations by raising more own capital. Finally, our study clarifies four issues: the efficiency of retail companies in a period of growth and a period of recession, focusing on SMEs that operate in different regions, connecting Accounting Equation and DEA and adding acid ratio as an output in our model.     

Investments in next generation access infrastructures under regulatory uncertainty

This article studies the impact of regulatory uncertainty on an incumbent’s incentives to undertake the socially optimal investments in NGA networks. Thus, a regulatory non-commitment setting in which the regulator sets the access price after the deployment of the NGA network is used. In particular, it is assumed that the regulator sets the access price at the marginal cost of providing the access with some probability and gives an access markup, which equals the average cost of the investments, with the complementary probability. It is found that when the slope of the marginal investment cost function is not particularly steep in relation to the impact of investments on demand, the incumbent underinvests compared to the socially optimal investment level. On the contrary, in a more realistic case when the impact of investments on demand is low in relation to the slope of the marginal investment cost function, the incumbent may overinvest or underinvest depending on the probability of incorporating an access markup into the access price.     

Firm efficiency and stock returns

In this paper, we investigate the role of firm efficiency in asset pricing using a sample of US publicly listed companies for the period 1988–2007. We employ non-parametric data envelopment analysis (DEA) on various input/output combinations, focusing on sales and market value as output measures in the construction of the frontier technologies. Using these performance measures, we examine whether efficient firms perform differently from inefficient firms following standard financial analysis procedures. First, we employ performance attribution regressions, by forming portfolios based on efficiency scores and tracking the performance of the various portfolios over time. Second, we perform cross-sectional/panel regressions to determine whether firm efficiency indeed has explanatory power for the cross-section of stock returns. Our results suggest that firm efficiency plays an important role in asset pricing and that efficient firms significantly outperform inefficient firms even after controlling for known risk factors.     

Empirical size and power of some diagnostic tests applied to a distributed lag model

We produce Monte Carlo evidence on the size and power of the RESET, a heteroscedasticity test, and a test for autocorrelation applied to realistic distributed-lag models. We find that the autocorrelation test has the correct size and high power to detect not only autocorrelation (given a correct model), but also the erroneous omission of several lags of an explanatory variable, whereas the RESET and heteroscedasticity tests are oversized in the presence of positive disturbance autocorrelation, especially when the regressors are also positively autocorrelated, and have no power to detect such misspecification errors. In large samples, size distortion may be avoided by using autocorrelation-robust methods.

On Regional Unemployment: An Empirical Examination of the Determinants of Geographical Differentials in the UK

In this paper we consider the determinants of regional disparities in unemployment rates for the UK regions at NUTS‐II level. We use a mixture panel data model to describe unemployment differentials between heterogeneous groups of regions. The results indicate the existence of two clusters of regions in the UK economy, characterized by high and low unemployment rates, respectively. A major source of heterogeneity appears to be caused by the varying effect (between the two clusters) of the share of employment in the service sector, and we trace its origin to the fact that the high unemployment cluster is characterized by a higher degree of urbanization.     

Productivity growth and inflation in Europe: Evidence from panel cointegration tests

In this paper we investigate the productivity growth – inflation nexus in fifteen European countries over the period 1961–1999 using panel unit root and panel cointegration tests. Emphasis is placed on the distinction between long-run and short-run causality using recently developed tests appropriate for heterogeneous panel. The empirical results are relevant for the role of the EMU and the Treaty of Maastricht in catching up, real convergence, and the future growth prospects of Europe. The policy implications of the findings are discussed in detail.     

The chief executive officer as corporate champion of technological innovation: aii empirical investigation

Research on innovation has engoged the attention of many scientific disciplines over the past few decades. But while some categories of determining factors (mainly organizational and environmental) are at the centre of this research, others, such as the role and significance of strategic leadership, seem to have received much less empirical attention. Using evidence from a sample of 97 manufacturing enterprises, the present study tests a model of the impact of strategic leadership and corporate context on technological innovatin (TT). The personality and demographic characteristics of chief executive officers (CEis') are used to measure strategic leadership. A number of environmental and internal organizational variables measure the broader context. Four dimensions of TI are measured. Results suggest that CEO characteristics significantly influence TI, but the structural and environmental context is on aggregate more influential. Interestingly though, in new product introductions, CEIs' characteristics outweight environmental and internal organizational factors.     

UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.