Steering the Sales Force for New Product Selling: Why Is it Different,and How Can Firms Motivate Different Sales Reps?

This study investigates how to direct and assemble the sales force for new product selling. In a first step, the authors draw on self‐determination theory to explore and empirically test a threefold conceptualization of motivation. Results provide insights into why sales force steering works differently in the new product selling context. Specifically, results show that for new products’ financial performance, internalized new product selling motivation is more important than intrinsic and controlled motivation. In a second step, the authors show how firms can motivate different sales reps to achieve higher financial performance of new products. In doing so, they examine the interaction effects of sales reps’ predispositions and widespread firm‐steering instruments on new products’ financial performance. Results reveal that the new product sales orientation of the bonus strengthens the positive relationship between sales reps’ performance predisposition and new product financial performance but weakens the relationship between sales reps’ learning predisposition and financial new product performance. Moreover, results reveal that the new product sales orientation of the periodic review strengthens the positive relationship between sales reps’ learning predisposition and financial new product performance. A post hoc analysis shows that a differentiated steering approach that matches appropriate steering instruments with sales reps’ varying predispositions substantially enhances reps’ financial new product performance.     

Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data

This paper discusses a factor model for short-term forecasting of GDP growth using a large number of monthly and quarterly time series in real-time. To take into account the different periodicities of the data and missing observations at the end of the sample, the factors are estimated by applying an EM algorithm, combined with a principal components estimator. We discuss some in-sample properties of the estimator in a real-time environment and propose alternative methods for forecasting quarterly GDP with monthly factors. In the empirical application, we use a novel real-time dataset for the German economy. Employing a recursive forecast experiment, we evaluate the forecast accuracy of the factor model with respect to German GDP. Furthermore, we investigate the role of revisions in forecast accuracy and assess the contribution of timely monthly observations to the forecast performance. Finally, we compare the performance of the mixed-frequency model with that of a factor model, based on time-aggregated quarterly data.     

Subsampling the distribution of diverging statistics with applications to finance

In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known or unknown rates when the underlying time series is strictly stationary and strong mixing. Based on our results we provide a detailed discussion of how to estimate extreme order statistics with dependent data and present two applications to assessing financial market risk. Our method performs well in estimating Value at Risk and provides a superior alternative to Hill's estimator in operationalizing Safety First portfolio selection.     

Discrete time Wishart term structure models

This paper reveals that the class of Affine Term Structure Models (ATSMs) introduced by Duffie and Kan (1996) is larger than previously considered in the literature. In the framework of risk factors following a Wishart autoregressive process, we define the Wishart Term Structure Model (WTSM) as an extension of a subclass of Quadratic Term Structure Models (QTSMs), derive simple parameter restrictions that ensure positive bond yields at all maturities, and observe that the usual constraint on affine processes requiring that the volatility matrix be diagonal up to a path independent linear invertible transformation can be considerably relaxed.