Efficient semiparametric estimation for endogenously stratified regression via smoothed likelihood

This paper presents efficient semiparametric estimators for endogenously stratified regression with two strata, in the case where the error distribution is unknown and the regressors are independent of the error term. The method is based on the use of a kernel-smoothed likelihood function which provides an explicit solution for the maximization problem for the unknown density function without losing information in the asymptotic limit. We consider both standard stratified sampling and variable probability sampling, and allow for the population shares of the strata to be either unknown or known a priori.     

The entrepreneurial motivation in academia: a multidimensional construct

In this paper we propose a model to analyse the motivation of academic entrepreneurs that comprises six dimensions: personal, relating to the entrepreneurial opportunity, to scientific knowledge, to the availability of resources, to the incubator organization, and to the social environment. The model is tested based on information from a survey administered to 152 Spanish academic entrepreneurs. Our results show that entrepreneurial opportunity is not part of the entrepreneurial motivation, but is of the utmost importance to academic entrepreneurs. Also, we find the scientific knowledge is the main driver of entrepreneurial activity in the academia.     

Hölder properties of local times for fractional Brownian motions

We study the local times of fractional Brownian motions for all temporal dimensions, N, spatial dimensions d and Hurst parameters H for which local times exist. We establish a Hölder continuity result that is a refinement of Xiao (Probab Th Rel Fields 109:129–157, 1997). Our approach is an adaptation of the more general attack of Xiao (Probab Th Rel Fields 109:129–157, 1997) using ideas of Baraka and Mountford (1997, to appear), the principal result of this latter paper is contained in this article.     

Linking improved knowledge management to operational and organizational performance

This research empirically examines the importance of knowledge management processes to operational and overall organizational performance (OPERF). Specifically, results indicate that a shared interpretation of knowledge among operational personnel mediates how knowledge is disseminated and used to design and implement a unified operational response to that knowledge. Further, results collected in a logistics operations (LO) context support a strong positive relationship between this knowledge management process and operational and organizational performance. Importantly, psychometric measures for organizational performance collected from managerial respondents were strongly correlated with secondary financial data for participating firms obtained from Compustat, thus supporting a link about operational performance and hard organizational performance data.     

The triple-edged sword of sensitivity of sales,cash flows,and debt to investments: Venture survival and capital investments

Drawing on reliable financial performance data of 192,855 venture-year observations, representing a total of 66,174 ventures with 8.13% of the ventures failing (5380 ventures), we find that neither sales-investment sensitivity nor cash-flow-investment sensitivity is associated with venture survival. However, debt-investment sensitivity lowers the hazard of failure. Sales-investment sensitivity and debt-investment sensitivity under munificence and dynamism lower the hazard of failure. However, cash-flow-investment sensitivity at high levels of dynamism or munificence does not influence the hazard of failure. The effect sizes are small but nonetheless meaningful. The findings have implications for ventures attempting to match performance and capital structure with investment.     

Confidence sets for the date of a break in level and trend when the order of integration is unknown

We propose methods for constructing confidence sets for the timing of a break in level and/or trend that have asymptotically correct coverage for both I(0) and I(1) processes. These are based on inverting a sequence of tests for the break location, evaluated across all possible break dates. We separately derive locally best invariant tests for the I(0) and I(1) cases; under their respective assumptions, the resulting confidence sets provide correct asymptotic coverage regardless of the magnitude of the break. We suggest use of a pre-test procedure to select between the I(0)- and I(1)-based confidence sets, and Monte Carlo evidence demonstrates that our recommended procedure achieves good finite sample properties in terms of coverage and length across both I(0) and I(1) environments. An application using US macroeconomic data is provided which further evinces the value of these procedures.     

Gendered giving: the influence of social norms on the donation behavior of men and women

• We study if men and women are subject to different influences when determining their level of charitable giving. In particular, we examine differential sensitivities to social norms among donors to a public radio station. Our survey of 975 donors finds that male donors are more likely than female donors to use social norms to inform their own behavior. We conduct a laboratory experiment to investigate the influence of social information on social norm formation and giving. Our results suggest that temporarily created social norms influence giving by men significantly more than by women. This result replicates and establishes the causal relationship between social norms and donations suggested by our field findings. We conclude with a discussion of theoretical and practical implications of these results.

Copyright © 2009 John Wiley & Sons, Ltd.     

Least squares model averaging by Mallows criterion

This paper is in response to a recent paper by Hansen (2007) who proposed an optimal model average estimator with weights selected by minimizing a Mallows criterion. The main contribution of Hansen’s paper is a demonstration that the Mallows criterion is asymptotically equivalent to the squared error, so the model average estimator that minimizes the Mallows criterion also minimizes the squared error in large samples. We are concerned with two assumptions that accompany Hansen’s approach. The first is the assumption that the approximating models are strictly nested in a way that depends on the ordering of regressors. Often there is no clear basis for the ordering and the approach does not permit non-nested models which are more realistic from a practical viewpoint. Second, for the optimality result to hold the model weights are required to lie within a special discrete set. In fact, Hansen noted both difficulties and called for extensions of the proof techniques. We provide an alternative proof which shows that the result on the optimality of the Mallows criterion in fact holds for continuous model weights and under a non-nested set-up that allows any linear combination of regressors in the approximating models that make up the model average estimator. These results provide a stronger theoretical basis for the use of the Mallows criterion in model averaging by strengthening existing findings.     

On the asymptotic optimality of the LIML estimator with possibly many instruments

We consider the estimation of the coefficients of a linear structural equation in a simultaneous equation system when there are many instrumental variables. We derive some asymptotic properties of the limited information maximum likelihood (LIML) estimator when the number of instruments is large; some of these results are new as well as old, and we relate them to results in some recent studies. We have found that the variance of the limiting distribution of the LIML estimator and its modifications often attain the asymptotic lower bound when the number of instruments is large and the disturbance terms are not necessarily normally distributed, that is, for the micro-econometric models of some cases recently called many instruments and many weak instruments.     

The LIML estimator has finite moments!

The Limited Information Maximum Likelihood estimator of the vector of coefficients of a structural equation in a simultaneous equation model is the vector that defines the linear combination maximizing the effect variance relative to the error variance. If this “eigenvector” solution is normalized by setting a designated coefficient equal to 1, the second-order moment of the estimator may be unbounded. However, the second-order moment is finite if the normalization sets the sample error variance of the linear combination equal to 1.