Does location influence executive compensation? Evidence from Canadian SMEs

We examine the impact of geographic location on the level and structure of executive compensation of small and medium enterprises (SMEs) in Canada, using a panel data sample between 2008 and 2011. Our results show that SMEs pay a higher price for talent by paying a large proportion (71%) of compensation as guaranteed cash pay to their executives. We also report a strong influence of location on compensation structure. Specifically, rural firms pay 13% more incentive based equity pay to their executives compared to their size matched urban counterparts. However, there is no difference between the total compensation for managers of rural and urban firms after controlling for the cost of living index. In cross-sectional tests, we observe that total compensation is positively related to CEO/Chair duality and family ownership but is not related to management quality. In addition, we find that rural firms display a higher pay-performance sensitivity.     

Detecting discontinuities in nonparametric regression curves and surfaces

The existence of a discontinuity in a regression function can be inferred by comparing regression estimates based on the data lying on different sides of a point of interest. This idea has been used in earlier research by Hall and Titterington (1992), Müller (1992) and later authors. The use of nonparametric regression allows this to be done without assuming linear or other parametric forms for the continuous part of the underlying regression function. The focus of the present paper is on assessing the evidence for the presence of a discontinuity within a regression function through examination of the standardised differences of ‘left’ and ‘right’ estimators at a variety of covariate values. The calculations for the test are carried out through distributional results on quadratic forms. A graphical method in the form of a reference band to highlight the sources of the evidence for discontinuities is proposed. The methods are also developed for the two covariate case where there are additional issues associated with the presence of a jump location curve. Methods for estimating this curve are also developed. All the techniques, for the one and two covariate situations, are illustrated through applications.     

Good Excuses: Understanding Who Votes With An Improved Turnout Question

Utilizing studies which validate voter turnout, previous researchershave been able to identify a strong tendency for individualsto report voting when they in fact did not. In this article,we assess the effectiveness of a new turnout question on reducingvoter over-reporting in the National Election Study. Providingrespondents with socially acceptable excuses for not voting,we found that this alternate question significantly reducesthe over-reporting of turnout in the 2002 National ElectionStudy by about 8 percentage points. Moreover, our analysis revealsthat with the new question wording, estimates of the turnoutrate for those usually thought to be the least likely to voteare considerably lower than estimates using the traditionalquestion. Thus, not only did the experiment work to significantlyreduce over-reporting, the new question provides deeper insightsinto the voting behavior of the American electorate that hasimplications for both scholars and reformers.     

Socio-ecological systems and biodiversity conservation in African city: Insights from Kano Emir’s Palace gardens

Urban biodiversity management in most African countries is at a peripheral stage. At the same time, African urban areas substantially remain poorly planned and intensively growing. This study explores the socio-ecological system as an alternative approach to explore African indigenous urban biodiversity management. For this purpose, we mixed conventional scientific investigation methods to explain the biodiversity conservation in the ancient gardens of the Kano palace in northern Nigeria. This multidisciplinary approach has successfully unveiled a harmonious relationship of humans and ecosystems in the context of African cultural values and heritage systems. The paper reveals that the palace gardens offer an avenue for intersection of a wide range of ecological and cultural activities. These include biodiversity conservation, recreation, security and surveillance, climate hazards proofing, and sacred dimensions. The findings of this study show that it is possible to address challenges confronting Africa’s urban biodiversity through the combination of scientific and indigenous systems.     

Penalized and Shrinkage Estimation in the Cox Proportional Hazards Model

This article considers the shrinkage estimation procedure in the Cox's proportional hazards regression model when it is suspected that some of the parameters may be restricted to a subspace. We have developed the statistical properties of the shrinkage estimators including asymptotic distributional biases and risks. The shrinkage estimators have much higher relative efficiency than the classical estimator, furthermore, we consider two penalty estimators—the LASSO and adaptive LASSO—and compare their relative performance with that of the shrinkage estimators numerically. A Monte Carlo simulation experiment is conducted for different combinations of irrelevant predictors and the performance of each estimator is evaluated in terms of simulated mean squared error. Simulation study shows that the shrinkage estimators are comparable to the penalty estimators when the number of irrelevant predictors in the model is relatively large. The shrinkage and penalty methods are applied to two real data sets to illustrate the usefulness of the procedures in practice.     

A class of distributions connected to order statistics with nonintegral sample size

Newton's binomial series expansion is used to develop a (class of) distribution function(s) F_r:∝ which may be interpreted as the distribution of the r^thorder statistic with nonintegral sample size∝. It is shown that F_r:∝ has properties similar to F_r:n. Multivariate extension is considered and an elementary proof of the integral representation for the joint distribution of a subset of order statistics is given. An application is included.     

Estimation of Slope for Linear Regression Model with Uncertain Prior Information and Student-t Error

This article considers estimation of the slope parameter of the linear regression model with Student-t errors in the presence of uncertain prior information on the value of the unknown slope. Incorporating uncertain non sample prior information with the sample data the unrestricted, restricted, preliminary test, and shrinkage estimators are defined. The performances of the estimators are compared based on the criteria of unbiasedness and mean squared errors. Both analytical and graphical methods are explored. Although none of the estimators is uniformly superior to the others, if the non sample information is close to its true value, the shrinkage estimator over performs the rest of the estimators.     

Fractionally integrated ARMA for crude palm oil prices prediction: case of potentially overdifference

Dealing with stationarity remains an unsolved problem. Some of the time series data, especially crude palm oil (CPO) prices persist towards nonstationarity in the long-run data. This dilemma forces the researchers to conduct first-order difference. The basic idea is that to obtain the stationary data that is considered as a good strategy to overcome the nonstationary counterparts. An opportune remark as it is, this proxy may lead to overdifference. The CPO prices trend elements have not been attenuated but nearly annihilated. Therefore, this paper presents the usefulness of autoregressive fractionally integrated moving average (ARFIMA) model as the solution towards the nonstationary persistency of CPO prices in the long-run data. In this study, we employed daily historical Free-on-Board CPO prices in Malaysia. A comparison was made between the ARFIMA over the existing autoregressive-integrated moving average (ARIMA) model. Here, we employed three statistical evaluation criteria in order to measure the performance of the applied models. The general conclusion that can be derived from this paper is that the usefulness of the ARFIMA model outperformed the existing ARIMA model.     

Trend,Growth Rate,and Change Point Analysis—A Data Driven Approach

A data-driven technique is proposed to estimate the trend and relative growth rate of time series data. The method is based on the local linear regression smoother and the only assumption about the form of the trend and growth rate function is that they are smooth functions of time. We also extended the method for handling sudden shifts or changes in the trend or growth rate functions by adding dummy variables for the jumps. Simulation studies are carried out to see the performance of the proposed procedure. The method is applied to study the trend and growth rate of wheat production in India from 1951–2005.     

A novel weighted likelihood estimation with empirical Bayes flavor

We propose a novel approach to estimation, where a set of estimators of a parameter is combined into a weighted average to produce the final estimator. The weights are chosen to be proportional to the likelihood evaluated at the estimators. We investigate the method for a set of estimators obtained by using the maximum likelihood principle applied to each individual observation. The method can be viewed as a Bayesian approach with a data-driven prior distribution. We provide several examples illustrating the new method and argue for its consistency, asymptotic normality, and efficiency. We also conduct simulation studies to assess the performance of the estimators. This straightforward methodology produces consistent estimators comparable with those obtained by the maximum likelihood method. The method also approximates the distribution of the estimator through the “posterior” distribution.