Bayesian Hierarchical Modelling for Tailoring Metric Thresholds

Software is highly contextual. While there are cross-cutting `global' lessons, individual software projects exhibit many `local' properties. This data heterogeneity makes drawing local conclusions from global data dangerous. A key research challenge is to construct locally accurate prediction models that are informed by global characteristics and data volumes. Previous work has tackled this problem using clustering and transfer learning approaches, which identify locally similar characteristics. This paper applies a simpler approach known as Bayesian hierarchical modeling. We show that hierarchical modeling supports cross-project comparisons, while preserving local context. To demonstrate the approach, we conduct a conceptual replication of an existing study on setting software metrics thresholds. Our emerging results show our hierarchical model reduces model prediction error compared to a global approach by up to 50%.Comment: Short paper, published at MSR '18: 15th International Conference on Mining Software Repositories May 28--29, 2018, Gothenburg, Swede

An Empirical Approach for Evaluating Soft Budget Constraints

In this paper, we develop an empirical framework for detecting the existence and estimating the magnitude of the softness of a budget constraint. The defining feature of a soft budget constraint is a subordinate organization’s expectations of being bailed out by a superior organization in case of financial trouble. This implies that one has to link the organization’s expectations for being bailed out to its fiscal behavior in order to quantify the extent of the soft budget constraint. We postulate that expectations for bailouts are formed rationally and make use of an instrumental variable method to get consistent estimates of the parameters of interest. We argue that past own experience of being bailed out and bailouts of other subordinate organizations can be used to construct credible instruments for the formation of bailout expectations. We apply our empirical approach to a unique panel data set of 286 Swedish local governments where the central government provided a total of 1,697 bailouts between 1974 and 1992. Our results strongly suggest the existence of a soft budget constraint; a local government increases its level of debt by 6-10 percent if it expects to be bailed out with probability one as compared to when the likelihood is zero due to previous experience of being bailed out, while the effect on debt from bailouts of its geographical neighbors is roughly four times as large.Soft budget constraint; Bailout; Fiscal distress; Intergovernmental relations

Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments

Segmentation in PLS path modeling framework results is a critical issue in social sciences. The assumption that data is collected from a single homogeneous population is often unrealistic. Sequential clustering techniques on the manifest variables level are ineffective to account for heterogeneity in path model estimates. Three PLS path model related statistical approaches have been developed as solutions for this problem. The purpose of this paper is to present a study on sets of simulated data with different characteristics that allows a primary assessment of these methodologies.Partial Least Squares; Path Modeling; Unobserved Heterogeneity