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The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

Literature has recognised entrepreneurship education as the main conduit through which entrepreneurial behaviours, attitudes and actions can be built, enacted and delivered. Since the founding of new ventures is largely a resourceful founder-driven enterprise, entrepreneurship education has largely centred on galvanising and shifting the mindsets and cognition of the entrepreneur. Yet, despite over 60 years of delivering entrepreneurship education programmes, hard evidence of the generation of high-growth-oriented and sustainable ventures has been scarce as student entrepreneurship intentions do not always translate into successful venture creation. This is largely because of the complexities of the practicality of entrepreneurial education particularly, the dissonance between acquired education in business schools and the knowledge and competencies needed in the entrepreneurial field. Such dissonance can be attributed to the lack of clarity on the pedagogical approach that most resonates with entrepreneurial action, the diversity in assessment methods and the scholarly illusion pertaining to how pedagogical approaches can be channelled to the generation of growth-oriented ventures. Drawing on Girox's concepts of transformative critical pedagogy (including pedagogy of repression), Socratic dialogue, Hegelian dialectic and Yrjö Engeström's transformative expansive agency, I demonstrate how a flipped transformative critical pedagogy can be harnessed in digitally enhanced learning environments to create new entrepreneurial possibilities for facilitating critical inquiry, complex problem-solving, innovation for the market and fostering tolerance for failure in ambiguous entrepreneurial contexts.

Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

Purpose: The availability of resilient energy infrastructure and services is crucial to achieving sustainable development goals. However, defined and trustworthy definitions of resilience exist solely for engineering and energy systems, particularly in the industrialised world or metropolitan systems. However, no universally accepted definition considers the distinctive characteristics of rural regions in developing economies. To define resilience for rural power systems in developing countries, this chapter synthesises many perspectives on resilience, energy systems, and rural environments.

Methodology: It draws on extensive literature assessments on resilience, particularly concerning energy systems and rural areas, as well as other pre-existing frameworks.

Findings: To account for the unique challenges of electricity supply in rural developing nations, a comprehensive ‘Rural Power System Resilience Framework’ is introduced, including technical, economic, and social resilience.

Social implications: To better understand the elements contributing to the stability of electricity grids in developing nations and rural areas, this resilience framework may be utilised by global markets, system owners and operators, government officials, non-governmental organisations, and communities.

Originality: Through establishing this framework, this study sets the path for developing suitable and ‘effective resilience standards’ tailored for implementation in these rural areas, with the ultimate goal of facilitating the fulfilment of achieving domestic and worldwide sustainability objectives.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.