Testing for the bivariate Poisson distribution

This paper studies goodness-of-fit tests for the bivariate Poisson distribution. Specifically, we propose and study several Cramér–von Mises type tests based on the empirical probability generating function. They are consistent against fixed alternatives for adequate choices of the weight function involved in their definition. They are also able to detect local alternatives converging to the null at a certain rate. The bootstrap can be used to consistently estimate the null distribution of the test statistics. A simulation study investigates the goodness of the bootstrap approximation and compares their powers for finite sample sizes. Extensions for testing goodness-of-fit for the multivariate Poisson distribution are also discussed.     

Nearly exact sample size calculation for powerful non‐randomized tests for differences between binomial proportions

In the case of two independent samples, it turns out that among the procedures taken in consideration, BOSCHLOO'S technique of raising the nominal level in the standard conditional test as far as admissible performs best in terms of power against almost all alternatives. The computational burden entailed in exact sample size calculation is comparatively modest for both the uniformly most powerful unbiased randomized and the conservative non‐randomized version of the exact Fisher‐type test. Computing these values yields a pair of bounds enclosing the exact sample size required for the Boschloo test, and it seems reasonable to replace the exact value with the middle of the corresponding interval. Comparisons between these mid‐N estimates and the fully exact sample sizes lead to the conclusion that the extra computational effort required for obtaining the latter is mostly dispensable. This holds also true in the case of paired binary data (McNemar setting). In the latter, the level‐corrected score test turns out to be almost as powerful as the randomized uniformly most powerful unbiased test and should be preferred to the McNemar–Boschloo test. The mid‐N rule provides a fairly tight upper bound to the exact sample size for the score test for paired proportions.     

Subsampling Inference with K Populations and a Non‐standard Behrens–Fisher Problem

We revisit the methodology and historical development of subsampling, and then explore in detail its use in hypothesis testing, an area which has received surprisingly modest attention. In particular, the general set‐up of a possibly high‐dimensional parameter with data from K populations is explored. The role of centring the subsampling distribution is highlighted, and it is shown that hypothesis testing with a data‐centred subsampling distribution is more powerful. In addition we demonstrate subsampling’s ability to handle a non‐standard Behrens–Fisher problem, i.e., a comparison of the means of two or more populations which may possess not only different and possibly infinite variances, but may also possess different distributions. However, our formulation is general, permitting even functional data and/or statistics. Finally, we provide theory for K ‐ sample U ‐ statistics that helps establish the asymptotic validity of subsampling confidence intervals and tests in this very general setting.     

Small‐sample testing inference in symmetric and log‐symmetric linear regression models

This paper deals with the issue of testing hypotheses in symmetric and log‐symmetric linear regression models in small and moderate‐sized samples. We focus on four tests, namely, the Wald, likelihood ratio, score, and gradient tests. These tests rely on asymptotic results and are unreliable when the sample size is not large enough to guarantee a good agreement between the exact distribution of the test statistic and the corresponding chi‐squared asymptotic distribution. Bartlett and Bartlett‐type corrections typically attenuate the size distortion of the tests. These corrections are available in the literature for the likelihood ratio and score tests in symmetric linear regression models. Here, we derive a Bartlett‐type correction for the gradient test. We show that the corrections are also valid for the log‐symmetric linear regression models. We numerically compare the various tests and bootstrapped tests, through simulations. Our results suggest that the corrected and bootstrapped tests exhibit type I probability error closer to the chosen nominal level with virtually no power loss. The analytically corrected tests as well as the bootstrapped tests, including the Bartlett‐corrected gradient test derived in this paper, perform with the advantage of not requiring computationally intensive calculations. We present a real data application to illustrate the usefulness of the modified tests.     

Multivariate residual‐based finite‐sample tests for serial dependence and ARCH effects with applications to asset pricing models

In this paper, we propose several finite‐sample specification tests for multivariate linear regressions (MLR). We focus on tests for serial dependence and ARCH effects with possibly non‐Gaussian errors. The tests are based on properly standardized multivariate residuals to ensure invariance to error covariances. The procedures proposed provide: (i) exact variants of standard multivariate portmanteau tests for serial correlation as well as ARCH effects, and (ii) exact versions of the diagnostics presented by Shanken ( 1990 ) which are based on combining univariate specification tests. Specifically, we combine tests across equations using a Monte Carlo (MC) test method so that Bonferroni‐type bounds can be avoided. The procedures considered are evaluated in a simulation experiment: the latter shows that standard asymptotic procedures suffer from serious size problems, while the MC tests suggested display excellent size and power properties, even when the sample size is small relative to the number of equations, with normal or Student‐t errors. The tests proposed are applied to the Fama–French three‐factor model. Our findings suggest that the i.i.d. error assumption provides an acceptable working framework once we allow for non‐Gaussian errors within 5‐year sub‐periods, whereas temporal instabilities clearly plague the full‐sample dataset. Copyright © 2009 John Wiley & Sons, Ltd.     

Bootstrap Co‐integration Rank Testing: The Effect of Bias‐Correcting Parameter Estimates

Bootstrap‐based methods for bias‐correcting the first‐stage parameter estimates used in some recently developed bootstrap implementations of co‐integration rank tests are investigated. The procedure constructs estimates of the bias in the original parameter estimates by using the average bias in the corresponding parameter estimates taken across a large number of auxiliary bootstrap replications. A number of possible implementations of this procedure are discussed and concrete recommendations made on the basis of finite sample performance evaluated by Monte Carlo simulation methods. The results show that bootstrap‐based bias‐correction methods can significantly improve the small sample performance of the bootstrap co‐integration rank tests.     

Bayesian Calibration of p‐Values from Fisher's Exact Test

p‐Values are commonly transformed to lower bounds on Bayes factors, so‐called minimum Bayes factors. For the linear model, a sample‐size adjusted minimum Bayes factor over the class of g‐priors on the regression coefficients has recently been proposed (Held & Ott, The American Statistician 70(4), 335–341, 2016). Here, we extend this methodology to a logistic regression to obtain a sample‐size adjusted minimum Bayes factor for 2 × 2 contingency tables. We then study the relationship between this minimum Bayes factor and two‐sided p‐values from Fisher's exact test, as well as less conservative alternatives, with a novel parametric regression approach. It turns out that for all p‐values considered, the maximal evidence against the point null hypothesis is inversely related to the sample size. The same qualitative relationship is observed for minimum Bayes factors over the more general class of symmetric prior distributions. For the p‐values from Fisher's exact test, the minimum Bayes factors do on average not tend to the large‐sample bound as the sample size becomes large, but for the less conservative alternatives, the large‐sample behaviour is as expected.     

Weak‐instrument robust inference for two‐sample instrumental variables regression

Instrumental variable (IV) methods for regression are well established. More recently, methods have been developed for statistical inference when the instruments are weakly correlated with the endogenous regressor, so that estimators are biased and no longer asymptotically normally distributed. This paper extends such inference to the case where two separate samples are used to implement instrumental variables estimation. We also relax the restrictive assumptions of homoskedastic error structure and equal moments of exogenous covariates across two samples commonly employed in the two‐sample IV literature for strong IV inference. Monte Carlo experiments show good size properties of the proposed tests regardless of the strength of the instruments. We apply the proposed methods to two seminal empirical studies that adopt the two‐sample IV framework.     

Multi‐state Stochastic Processes: A Statistical Flowgraph Perspective

Two‐state models (working/failed or alive/dead) are widely used in reliability and survival analysis. In contrast, multi‐state stochastic processes provide a richer framework for modeling and analyzing the progression of a process from an initial to a terminal state, allowing incorporation of more details of the process mechanism. We review multi‐state models, focusing on time‐homogeneous semi‐Markov processes (SMPs), and then describe the statistical flowgraph framework, which comprises analysis methods and algorithms for computing quantities of interest such as the distribution of first passage times to a terminal state. These algorithms algebraically combine integral transforms of the waiting time distributions in each state and invert them to get the required results. The estimated transforms may be based on parametric distributions or on empirical distributions of sample transition data, which may be censored. The methods are illustrated with several applications.     

Evaluating Form and Functionality of Pay‐for‐Performance Plans: The Relative Incentive and Sorting Effects of Merit Pay,Bonuses, and Long‐Term Incentives

Using two‐year longitudinal data from a large sample of US employees from a service‐related organization, the present study investigates the relative effects of three forms of pay‐for‐performance (PFP) plans on employees’ job performance (incentive effects) and voluntary turnover (sorting effects). The study differentiates between three forms of pay: merit pay, individual‐based bonuses, and long‐term incentives. By definition, these PFP plans have different structural elements that distinguish them from each other (i.e., pay plan form) and different characteristics (functionality), such as the degree to which pay and performance are linked and the size of the rewards, which can vary both within and across plan types. Our results provide evidence that merit raises have larger incentive and sorting effects than bonuses and long‐term incentives in multi‐PFP plan environments where the three PFP plans are operating simultaneously. Only merit pay has both incentive and sorting effects among the three PFP plans. The implications for the PFP‐related theory, as well as for the design and implementation of PFP plans, are discussed. © 2015 Wiley Periodicals, Inc.     

Exact Skewness–Kurtosis Tests for Multivariate Normality and Goodness‐of‐Fit in Multivariate Regressions with Application to Asset Pricing Models*

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross‐equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared with a simulation‐based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal and stable error distributions. In the Gaussian case, finite‐sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi‐stage Monte Carlo test methods. For non‐Gaussian distribution families involving nuisance parameters, confidence sets are derived for the nuisance parameters and the error distribution. The tests are applied to an asset pricing model with observable risk‐free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over 5‐year subperiods from 1926 to 1995.     

Critically Interrogating Eco‐Homes

Eco‐homes have only been researched in fragmented and partial ways, which fail to adequately examine their complexities and possibilities. Numerous myths about eco‐homes persist in the public imagination and policy support has been mixed with, in practice, little change to the construction of contemporary homes. The ecological and social potential of eco‐homes are being undermined by a technocratic focus, the capacity and behaviour of occupants, and a weakening of design as developments are scaled up. This intervention identifies five ways in which eco‐homes need to be more robustly interrogated to strengthen their potential, through their breadth and diversity, dynamic nature, socio‐material interdependencies, place, and understanding of their political economies. Crucially these interrogations need to be researched simultaneously to ensure that the full diversity of eco‐homes is understood through their multiple interdependencies, multi‐scalar practices and materialities.     

Mutual Gains? Health‐Related HRM,Collective Well‐Being and Organizational Performance

Research on the effects of HR management on employees’ psychological well‐being has yielded inconclusive results. Moreover, prior works remain unclear on whether human resource practices specifically aimed at enhancing employee well‐being also benefit organizational performance. Building on signaling theory and conservation of resources theory, our study investigates the relationship between health‐related human resource management (HHRM), employees’ collective well‐being (in terms of collective emotional exhaustion and collective engagement) and organizational performance. Results from a multi‐source field study of top management team members, HR representatives, and 15,952 employees in 88 organizations reveal a positive indirect relationship between HHRM and employees’ collective well‐being, which is mediated by employees’ positive stress mindset. In addition, we find this positive indirect association to depend on the level of transformational leadership climate in organizations. Finally, our findings also show a positive indirect relationship between HHRM and company performance, mediated by employees’ positive stress mindset and collective engagement.     

Exercise as a Means of Reducing Perceptions of Work‐Family Conflict: A Test of the Roles of Self‐Efficacy and Psychological Strain

Prior research suggests that exercise has positive benefits for individuals and the organizations they work for. Using the literature related to social cognitive theory and exercise, the present study hypothesizes and tests the effects of exercise on two directions of work‐family conflict: work interference with family (WIF) and family interference with work (FIW). Using a sample of 476 working adults, we found that exercise was both positively related to self‐efficacy for managing work‐family conflict and negatively related to reported psychological strain, which in turn were related to reductions in work‐family conflict. Model fit for the data support our contention that exercise indirectly influences work‐family conflict via its direct impact on increased self‐efficacy and reduced psychological strain. © 2014 Wiley Periodicals, Inc.     

Alignment,integration, and consistency in HRM across multi‐employer networks

Most studies of human resource management (HRM) have been conducted within the context of the single employing organization, which is strange given the recent growth in multi‐employer networks. In this study, the authors examine whether alignment, integration, and consistency—concepts central to or implicit in most analyses of HRM—has meaning and relevance in the multi‐employer context. They focus specifically on networks in which collaboration is intended to deliver high levels of product quality or customer service, precisely where one might expect employers would be attracted to “strong” HRM systems. Data was collected via interviews and document analysis in four networks, spanning both the public and private sectors in the United Kingdom. Despite a set of potentially favorable conditions within these networks to promote alignment, integration, and consistency, implementation was impeded by other equally powerful forces, including differences in employer goals within networks, especially between public and private sector organizations; intraorganizational tensions within internal labor markets for organizations involved in networks; using divergent HR policies between organizations within multi‐employer networks; and contradictions between the pursuit of “among employee” or “temporal” consistency for workers. Rather than prescribing a one‐size‐fits‐all solution for these problems, it is argued that detailed analysis of each network is necessary. ©2011 Wiley Periodicals, Inc.     

On the comprehensive method of testing non-nested regression models

The paper derives the specific form of the exponentially combined likelihood function of two competing multivariate non-linear regression models and shows that the application of the comprehensive approach to testing non-nested regression models will, in general, be indeterminate. It establishes that in the univariate case there exists a large number of tests of non-nested regression models which are consistent in addition to having the same asymptotic distribution under the null hypothesis. The paper then derives a set of conditions under which all these consistent tests are asymptotically equivalent not only under the null hypothesis but also under local alternatives. As an application of this latter result the paper establishes the asymptotic equivalence of the tests recently proposed by Davidson and MacKinnon, and Fisher and McAleer under local alternatives, and shows that within the class of tests considered in the paper these proposed tests possess maximum local power. The latter test has this property only when the number of explanatory variables of the ‘true’ model is not more than that of the ‘false’ model.     

Controlling the Urban Fabric: The Complex Game of Distance and Proximity in European Upper‐Middle‐Class Residential Strategies

This article presents an open discussion of the processes of urban secession and gentrification in contemporary European cities, arguing that intergroup social dynamics in urban spaces are generally more complex than either extreme mutual avoidance or the colonization of neighbourhoods by the wealthiest groups. We analyse the residential strategies of urban upper‐middle class managers in various European metropolitan areas through in‐depth semi‐structured interviews to argue that these groups develop complex strategies of proximity and distance in relation to other social groups. The development of these ‘partial exit’ strategies takes place through specific combinations of practices that allow groups to select the dimensions they are willing to share with other social groups, and those in which they prefer a more segregated social environment for themselves and their families. The responses of our interviewees were consistently more nuanced and complex than suggested by a simplistic theory about their drive to withdraw from society, forcing us to develop more sophisticated conceptual frameworks to account for the growing prevalence of multi‐layered identities and spheres of reference and solidarity, specific combinations of elective segregation and local involvement, and more active patterns of mobility combined with local embeddedness.     

CHOOSING AMONG ALTERNATIVE LONG‐RUN EVENT‐STUDY TECHNIQUES

This paper reviews the long‐run event‐study debate by outlining the strengths and weakness of the most commonly used alternative techniques. The fist part of the discussion highlights that prior literature has failed to provide a single risk‐adjusted model of long‐run abnormal returns with no biases. Subsequently, the paper provides guidance on how one can choose among pertinent alternative techniques. As a conclusion, researchers ought to choose among alternative techniques after considering issues such as (i) the nature of dataset and market of interest, (ii) the event type (regulatory or corporate), (iii) returns’ time‐interval, (iv) association of the event with accounting data, (v) sample characteristics and prior evidence regarding similar events, as well as (vi) risk changes following the event. Robustness tests are essential, while the road for further research regarding the appropriate technique(s) is open.     

Asymptotic comparison at optimal levels of reduced‐bias extreme value index estimators

In this article we are interested in the asymptotic comparison, at optimal levels, of a set of semi‐parametric reduced‐bias extreme value (EV) index estimators, valid for a wide class of heavy‐tailed models, underlying the available data. Again, as in the classical case, there is not any estimator that can always dominate the alternatives, but interesting clear‐cut patterns are found. Consequently, and in practice, a suitable choice of a set of EV index estimators will jointly enable us to better estimate the EV index γ, the primary parameter of extreme events.