Resource allocation strategy for innovation portfolio management

Our study demonstrates empirically that the choice of resource allocation strategy affects innovation performance. Allocating resources to a broader range of innovation projects increases new product sales, an effect that appears to outweigh that of resource intensity. In addition, we find that the performance benefit of breadth is higher for firms that allocate resources selectively at later stages of the innovation process. This breadth‐selectiveness effect is greatest for firms intending to create relatively more novel products, departing further from their knowledge base. Based on these results, we theorize that breadth increases performance because it spreads firms' bets on unproven innovative endeavors. Limiting resource commitments by selecting out deteriorating projects prevents an escalation in the costs of breadth. This advantage increases with the uncertainty implicit in greater innovative intent. The paper thus contributes to theory of how resource allocation strategies influence performance outcomes of innovation project portfolios. Copyright © 2013 John Wiley & Sons, Ltd.     

With or without you: market quality of floor trading when screen trading closes early

Most stock markets are characterized by a number of parallel operating trading systems which interact intensively with each other. Usually, smaller trading platforms take the leading domestic main market as a benchmark in the price discovery process and for closing open trading positions. But what happens if the smaller trading systems suddenly have to act without this benchmark platform? We examine the effects of the reduction of the daily business hours of a screen based main trading system while a parallel floor based trading system keeps on operating. We provide evidence that liquidity improves while informed trading and informational efficiency of prices decrease at the floor based trading system as a result of the no longer operating main market. While prior research on parallel trading focuses on changes due to a growing number of trading venues, we present the first evidence on market effects when the main trading platform reduces trading hours.     

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.     

Location decisions of knowledge-based entrepreneurs: Why some Catalan KISAs choose to be rural?

Entrepreneurship has currently become an important element of economic development and innovation policy. In this context, the promotion of knowledge-based firms has become the norm, even in rural areas. Thus, the study presented in this paper analyses the variables that influence the choice of location made by rural and urban knowledge intensive service activity firms (KISA, hereafter). The results of the quantitative study allows for important policy making recommendations, but also offers significant contributions for entrepreneurship and regional development researchers, as well as practical insights for entrepreneurs.     

Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP*

In this article, we merge two strands from the recent econometric literature. First, factor models based on large sets of macroeconomic variables for forecasting, which have generally proven useful for forecasting. However, there is some disagreement in the literature as to the appropriate method. Second, forecast methods based on mixed‐frequency data sampling (MIDAS). This regression technique can take into account unbalanced datasets that emerge from publication lags of high‐ and low‐frequency indicators, a problem practitioner have to cope with in real time. In this article, we introduce Factor MIDAS, an approach for nowcasting and forecasting low‐frequency variables like gross domestic product (GDP) exploiting information in a large set of higher‐frequency indicators. We consider three alternative MIDAS approaches (basic, smoothed and unrestricted) that provide harmonized projection methods that allow for a comparison of the alternative factor estimation methods with respect to nowcasting and forecasting. Common to all the factor estimation methods employed here is that they can handle unbalanced datasets, as typically faced in real‐time forecast applications owing to publication lags. In particular, we focus on variants of static and dynamic principal components as well as Kalman filter estimates in state‐space factor models. As an empirical illustration of the technique, we use a large monthly dataset of the German economy to nowcast and forecast quarterly GDP growth. We find that the factor estimation methods do not differ substantially, whereas the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the Factor MIDAS models, which confirms the usefulness of the mixed‐frequency techniques that can exploit timely information from business cycle indicators.     

Inconsistency of the MLE and inference based on weighted LS for LARCH models

This paper considers a class of finite-order autoregressive linear ARCH models. The model captures the leverage effect, allows the volatility to be arbitrarily close to zero and to reach its minimum for non-zero innovations, and is appropriate for long memory modeling when infinite orders are allowed. However, the (quasi-)maximum likelihood estimator is, in general, inconsistent. A self-weighted least-squares estimator is proposed and is shown to be asymptotically normal. A score test for conditional homoscedasticity and diagnostic portmanteau tests are developed. Their performance is illustrated via simulation experiments. It is also investigated whether stock market returns exhibit some of the characteristic features of the linear ARCH model.