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Purpose Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling. Design/methodology/approach Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results. Findings This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models. Research limitations/implications The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM. Practical implications Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities. Originality/value This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature.

We examine the equilibrium effects of college financial aid policies building an overlapping-generations life cycle model with education, labor supply, and saving decisions. Cognitive and noncognitive skills of children depend on parental education and skills and affect education and labor market outcomes. Education is funded by parental transfers that supplement grants, loans, and student labor supply. Crowding out of parental transfers by government programs is sizable and cannot be ignored. The current system of federal aid improves long-run welfare by 6 percent. More generous ability-tested grants would increase welfare and dominate both an expansion of student loans and a labor tax cut.

Purpose This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM. Design/methodology/approach This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models. Findings This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables. Research limitations/implications Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously. Practical implications To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables. Originality/value This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

The high-temperature, all-inorganic CsPbI₃ perovskite black phase is metastable relative to its yellow, nonperovskite phase at room temperature. Because only the black phase is optically active, this represents an impediment for the use of CsPbI₃ in optoelectronic devices. We report the use of substrate clamping and biaxial strain to render black-phase CsPbI₃ thin films stable at room temperature. We used synchrotron-based, grazing incidence, wide-angle x-ray scattering to track the introduction of crystal distortions and strain-driven texture formation within black CsPbI₃ thin films when they were cooled after annealing at 330°C. The thermal stability of black CsPbI₃ thin films is vastly improved by the strained interface, a response verified by ab initio thermodynamic modeling.

Hybrid nanowires combining semiconductor and superconductor materials appear well suited for the creation, detection, and control of Majorana bound states (MBSs). We demonstrate the emergence of MBSs from coalescing Andreev bound states (ABSs) in a hybrid InAs nanowire with epitaxial Al, using a quantum dot at the end of the nanowire as a spectrometer. Electrostatic gating tuned the nanowire density to a regime of one or a few ABSs. In an applied axial magnetic field, a topological phase emerges in which ABSs move to zero energy and remain there, forming MBSs. We observed hybridization of the MBS with the end-dot bound state, which is in agreement with a numerical model. The ABS/MBS spectra provide parameters that are useful for understanding topological superconductivity in this system.

The method of model averaging has become an important tool to deal with model uncertainty, for example in situations where a large amount of different theories exist, as are common in economics. Model averaging is a natural and formal response to model uncertainty in a Bayesian framework, and most of the paper deals with Bayesian model averaging. The important role of the prior assumptions in these Bayesian procedures is highlighted. In addition, frequentist model averaging methods are also discussed. Numerical techniques to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on uncertainty regarding the choice of covariates in normal linear regression models, but the paper also covers other, more challenging, settings, with particular emphasis on sampling models commonly used in economics. Applications of model averaging in economics are reviewed and discussed in a wide range of areas including growth economics, production modeling, finance and forecasting macroeconomic quantities.

Lake ecosystems, and the organisms that live within them, are vulnerable to temperature change 1 – 5 , including the increased occurrence of thermal extremes 6 . However, very little is known about lake heatwaves—periods of extreme warm lake surface water temperature—and how they may change under global warming. Here we use satellite observations and a numerical model to investigate changes in lake heatwaves for hundreds of lakes worldwide from 1901 to 2099. We show that lake heatwaves will become hotter and longer by the end of the twenty-first century. For the high-greenhouse-gas-emission scenario (Representative Concentration Pathway (RCP) 8.5), the average intensity of lake heatwaves, defined relative to the historical period (1970 to 1999), will increase from 3.7 ± 0.1 to 5.4 ± 0.8 degrees Celsius and their average duration will increase dramatically from 7.7 ± 0.4 to 95.5 ± 35.3 days. In the low-greenhouse-gas-emission RCP 2.6 scenario, heatwave intensity and duration will increase to 4.0 ± 0.2 degrees Celsius and 27.0 ± 7.6 days, respectively. Surface heatwaves are longer-lasting but less intense in deeper lakes (up to 60 metres deep) than in shallower lakes during both historic and future periods. As lakes warm during the twenty-first century 7 , 8 , their heatwaves will begin to extend across multiple seasons, with some lakes reaching a permanent heatwave state. Lake heatwaves are likely to exacerbate the adverse effects of long-term warming in lakes and exert widespread influence on their physical structure and chemical properties. Lake heatwaves could alter species composition by pushing aquatic species and ecosystems to the limits of their resilience. This in turn could threaten lake biodiversity 9 and the key ecological and economic benefits that lakes provide to society. Modelling and remote sensing show that by the end of the twenty-first century, lake heatwaves will be several degrees Celsius warmer and some will be months longer, with potentially major adverse consequences for lake ecosystems.

Coal dust is a great threat to coal mine workers' health and safety in coal mine production. Wet dust removal is one of the effective dust removal methods. As a solid, coal has different rough surfaces, which have a certain effect on the wetting effect of coal. In this paper, three coal samples with different surface wettability are used as the research objects. Phase-field interface tracking method is used to simulate the wetting of droplets on rough surfaces. From the simulation results, it can be concluded that the influence of the rough interface on the contact angle of the droplets is in accordance with the change rule described in the Wenzel model. As the roughness increases, the contact angle of the hydrophilic lignite surface gradually decreases. As the roughness increases, the contact angle of hydrophobic coking coal gradually increases. The change trend of the contact on the surface of weakly hydrophilic anthracite coal is the same as that of lignite. Due to the local and global differences, the contact angles obtained from the numerical model are slightly different from the values calculated from the Wenzel model.

As a result of the analysis of the application of numerical modeling in modern studies of the cumulation process, it is proposed to isolate the estimated parameters at the time of the formation of a shaped jet as an effective way to reduce the resource intensity of calculations. The numerical method is used to study the influence of the microstructure of the cumulative lining material on the efficiency of jet formation. The following parameters are proposed: the angle between the reduced velocity vector of the jet head and the charge axis γ, reflecting the efficiency of the deformation of the cumulative jet material, and the collapse angle of the cumulative lining γ′, reflecting the efficiency of the deformation of the cumulative jet material. The results of the calculation correlate with the known data obtained by other methods, and this confirms the prospects of the chosen direction of the research.