THE FAILURE TO PREDICT THE GREAT RECESSION—A VIEW THROUGH THE ROLE OF CREDIT

Much has been written about why economists failed to predict the latest crisis. Reading the literature, it seems that this crisis was so obvious that economists must have been blind not to see it coming. We approach this failure by looking at one of the key variables in this analysis, the evolution of credit. We compare the conclusions reached in the recent literature with those that could have been drawn from an ex‐ante analysis. We show that the effect of credit on the business cycle cannot be exploited from a policymaker's point of view.     

Inference for $$L_2$$L2 -Boosting

We propose a statistical inference framework for the component-wise functional gradient descent algorithm (CFGD) under normality assumption for model errors, also known as $$L_2$$-Boosting. The CFGD is one of the most versatile tools to analyze data, because it scales well to high-dimensional data sets, allows for a very flexible definition of additive regression models and incorporates inbuilt variable selection. Due to the variable selection, we build on recent proposals for post-selection inference. However, the iterative nature of component-wise boosting, which can repeatedly select the same component to update, necessitates adaptations and extensions to existing approaches. We propose tests and confidence intervals for linear, grouped and penalized additive model components selected by $$L_2$$-Boosting. Our concepts also transfer to slow-learning algorithms more generally, and to other selection techniques which restrict the response space to more complex sets than polyhedra. We apply our framework to an additive model for sales prices of residential apartments and investigate the properties of our concepts in simulation studies.     

Collaboration and Internationalization of SMEs: Insights and Recommendations from a Systematic Review

This paper performs a systematic literature review of the undeniably diverse – and somewhat fragmented – current state of research on the collaborations and internationalization of small and medium‐sized enterprises (SMEs). We analyze key works and synthesize them into a framework that conceptually maps key antecedents, mediators, and moderators that influence the internationalization of SMEs. In addition, we highlight limitations of the literature, most notably in terms of theoretical fragmentation; extant theories are deployed and illustrated but rarely extended in a manner that significantly informs subsequent work. At an applied (but related) level, we argue the need for supplementary work that explores the distinct stages of internationalization – and the scope and scale of this process – rather than assuming closure around particular events. With this, we highlight the need for more rigorous and empirically informed explorations of contextual effects that take account of the consequences of developments in the global economic ecosystem.     

Empirical likelihood based detection procedure for change point in mean residual life functions under random censorship

The mean residual life (MRL) function is one of the basic parameters of interest in survival analysis that describes the expected remaining time of an individual after a certain age. The study of changes in the MRL function is practical and interesting because it may help us to identify some factors such as age and gender that may influence the remaining lifetimes of patients after receiving a certain surgery. In this paper, we propose a detection procedure based on the empirical likelihood for the changes in MRL functions with right censored data. Two real examples are also given: Veterans' administration lung cancer study and Stanford heart transplant to illustrate the detecting procedure. Copyright © 2016 John Wiley & Sons, Ltd.     

Subgroup Analysis with Time‐to‐Event Data Under a Logistic‐Cox Mixture Model

Subgroup detection has received increasing attention recently in different fields such as clinical trials, public management and market segmentation analysis. In these fields, people often face time‐to‐event data, which are commonly subject to right censoring. This paper proposes a semiparametric Logistic‐Cox mixture model for subgroup analysis when the interested outcome is event time with right censoring. The proposed method mainly consists of a likelihood ratio‐based testing procedure for testing the existence of subgroups. The expectation–maximization iteration is applied to improve the testing power, and a model‐based bootstrap approach is developed to implement the testing procedure. When there exist subgroups, one can also use the proposed model to estimate the subgroup effect and construct predictive scores for the subgroup membership. The large sample properties of the proposed method are studied. The finite sample performance of the proposed method is assessed by simulation studies. A real data example is also provided for illustration.     

Collective versus Individual Effects in Survival Analysis of Multiple Failures

In survival analysis, we sometimes encounter data with multiple censored outcomes. Under certain scenarios, partial or even all covariates have ‘similar’ relative risks on the multiple outcomes in the Cox regression analysis. The similarity in covariate effects can be quantified using the proportionality of regression coefficients. Identifying the proportionality structure, or equivalently whether covariates have individual or collective effects, may have important scientific implications. In addition, it can lead to a smaller set of unknown parameters, which in turn results in more accurate estimation. In this article, we develop a novel approach for identifying the proportionality structure. Simulation shows the satisfactory performance of the proposed approach and its advantage over estimation under no assumed structure. We analyse three datasets to demonstrate the practical application of the proposed approach.     

A Necessary Condition for the Strong Oracle Property

A problem of using a non‐convex penalty for sparse regression is that there are multiple local minima of the penalized sum of squared residuals, and it is not known which one is a good estimator. The aim of this paper is to give a guide to design a non‐convex penalty that has the strong oracle property. Here, the strong oracle property means that the oracle estimator is the unique local minimum of the objective function. We summarize three definitions of the oracle property – the global, weak and strong oracle properties. Then, we give sufficient conditions for the weak oracle property, which means that the oracle estimator becomes a local minimum. We give an example of non‐convex penalties that possess the weak oracle property but not the strong oracle property. Finally, we give a necessary condition for the strong oracle property.     

Visualization for Large‐scale Gaussian Updates

In geostatistics and also in other applications in science and engineering, it is now common to perform updates on Gaussian process models with many thousands or even millions of components. These large‐scale inferences involve modelling, representational and computational challenges. We describe a visualization tool for large‐scale Gaussian updates, the ‘medal plot’. The medal plot shows the updated uncertainty at each observation location and also summarizes the sharing of information across observations, as a proxy for the sharing of information across the state vector (or latent process). As such, it reflects characteristics of both the observations and the statistical model. We illustrate with an application to assess mass trends in the Antarctic Ice Sheet, for which there are strong constraints from the observations and the physics.