Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.     

Life Cycle Characteristics of Small Professional Service Firms*

Our study of professional services firms clearly revealed that firms change over the course of their life cycles. During the first three stages, diversification in sales, the differentiation in labor force, and the level of labor productivity increase. In the last stage, diversification in sales, the differentiation in labor force, and the level of labor productivity decrease. Development effects can be treated as interdependent. Our findings show that the effects of growth can be predicted to a certain extent. In efforts towards development, these predictions are important to business strategy, as well as to consultation with and education of SME entrepreneurs.     

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Breeding places for ethnic entrepreneurs: a comparative marketing approach

This paper aims to examine the performance conditions of ethnic (migrant) entrepreneurs in a modern economy. After a broad overview of key issues, an analytical tool from marketing theory is proposed, based on the five Ps (Product, Price, Place, Personnel and Promotion). Next, an empirical application is presented, in which results from an in-depth interview study on Moroccan entrepreneurs in Amsterdam are discussed. Given the linguistic and qualitative information in our data base, two recently developed pattern recognition methods for categorized information, namely Apriori and rough set methods, are deployed in order to derive meaningful association and classification rules that are helpful to identify conditional success or performance rules.     

Impact of farm management diversity on the environmental and economic performance of the wheat–maize cropping system in the North China Plain

To assess the attainable greenhouse gas (GHG) mitigation potential from crop production in China's most important grain production region, the North China Plain (NCP), single farm environmental and economic performances of 65 winter wheat–summer maize (WW–SM) producing households were determined. The results revealed a huge heterogeneity among farms, with up to five times higher environmental impact of worst compared to best performing farms. Astonishingly no trade-off between productivity and sustainability could be identified in the region, with high-yield farms emitting no different amounts of GHGs per hectare compared to low-yield farms. Building on cluster analysis, with farms grouped according to their economic and environmental performance into ‘poor’, ‘fair' and ‘good’ producers, the regional GHG mitigation potential was estimated. Under the scenario assumption that all grain in the NCP is produced under ‘good’ production conditions, 21% and 7% of GHG could be mitigated in wheat and maize production, respectively. The study shows that in the NCP, exemplary for China's rapidly developing agricultural sector, the crop management skills of a substantial share of farmers could obviously not keep pace with the massive input intensification. Among others, farmer–farmer trainings are recommended to close the gap in crop production performance among producers.     

Why SMEs invest in environmental measures: sustainability evidence from small and medium‐sized printing firms

Why do SMEs invest in environmental measures? From the literature we know that most SMEs are rather slow in adopting these sustainable measures, but then the following question arises: why are other SMEs fast in this respect? From our research, it becomes clear that improving the working conditions is the most important reason why these fast‐going SMEs invest in environmental measures, as this probably improves their employees' motivation and performance. This explanation connects ‘planet’ and ‘people’ with each other, and does not exclude ‘profit’, which completes the traditional sustainability circle. Copyright © 2006 John Wiley & Sons, Ltd and ERP Environment.     

Nonparametric transformation to white noise

We consider a semiparametric distributed lag model in which the “news impact curve” m is nonparametric but the response is dynamic through some linear filters. A special case of this is a nonparametric regression with serially correlated errors. We propose an estimator of the news impact curve based on a dynamic transformation that produces white noise errors. This yields an estimating equation for m that is a type two linear integral equation. We investigate both the stationary case and the case where the error has a unit root. In the stationary case we establish the pointwise asymptotic normality. In the special case of a nonparametric regression subject to time series errors our estimator achieves efficiency improvements over the usual estimators, see Xiao et al. [2003. More efficient local polynomial estimation in nonparametric regression with autocorrelated errors. Journal of the American Statistical Association 98, 980–992]. In the unit root case our procedure is consistent and asymptotically normal unlike the standard regression smoother. We also present the distribution theory for the parameter estimates, which is nonstandard in the unit root case. We also investigate its finite sample performance through simulation experiments.     

The role of planning in small firms: Empirical evidence from a service industry

The relation between planning behaviour and performance is analyzed for a large number of small real estate firms in the Netherlands. A firm's planning behaviour appears to be influenced by the way it perceives its environment (in terms of complexity and dynamics) as well as its own characteristics (size, age and activities). The explanation of performance is dominated by a firm's size and the nature of its activities. Hardly and effect of planning on a firm's performance is found, though it appears that in an environment perceived as highly dynamic, planning is merely counter-productive.The authors wish to thank Jan Eppink and Ingrid van Rijn, and are especially grateful to Roy Thurik and two anonymous referees for valuable comments on earlier drafts. It goes without saying that errors and omissions remain the full responsibility of the authors.     

Response pattern analysis: Assuring data integrity in extreme research settings

Research summary : Strategy scholars increasingly conduct research in nontraditional contexts. Such efforts often require the assistance of third‐party intermediaries who understand local culture, norms, and language. This reliance on intermediation in primary or secondary data collection can elicit agency breakdowns that call into question the reliability, analyzability, and interpretability of responses. Herein, we investigate the causes and consequences of intermediary bias in the form of faked data and we offer Response Pattern Analysis as a statistical solution for identifying and removing such problematic data. By explicating the effect, illustrating how we detected it, and performing a controlled field experiment in a developing country to test the effectiveness of our methodological solution, we encourage researchers to continue to seek data and build theory from unique and understudied settings. Managerial summary : Any form of survey research contains the risk of interviewers faking data. This risk is particularly difficult to mitigate in Base‐of‐Pyramid or developing country contexts where researchers have to rely on intermediaries and forms of control are limited. We provide a statistical technique to identify a faking interviewer's ex post data collection, and remove the associated data prior to analysis. Using a field experiment where we instruct interviewers to fake the data, we demonstrate that the algorithm we employ achieves a 90 percent accuracy in terms of differentiating faking from nonfaking interviewers. Copyright © 2016 John Wiley & Sons, Ltd.