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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.

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

The turbulent phase of COVID-19 has caused uncertainty as governments fail to develop coherent strategies for cutting emissions and are struggling to match the rhetoric of sustainable activities with actions (Barbier & Burgess, 2020; Cawthorn, Kennaugh, & Ferreira, 2021). In the recent past, firms have failed in their plans to decarbonise their key sectors such as the retail sector in the United Kingdom So far, retailers' commitment to achieving net zero emissions has been an important pledge but delivery is nowhere closer to their promises (Henriques, 2020). The firms' climate targets are not going to be met by magic as serious action is needed to fulfil the promises.

Fossil fuels have led to a drastic increase in carbon emissions in the world over the last decade. Firms championing cleaner energy and low carbon technologies are needed to cut emissions. Renewable energy sources such as wind energy can help reducing the dependency of fossil fuels (Boretti, 2020; Ebhota & Jen, 2020). Wind is an indirect form of solar energy which can provide environment-friendly option in uncertain times and can provide long-term sustainability of global economy. Solar energy technologies have the potential to decrease climate change through energy-related emissions (Li, Dai, & Cui, 2020). Increasing energy demand has initiated a focus on using hydrogen from water as a substitute for oil and fossil fuels (Boretti, 2020).

The first part of the chapter discusses theoretical perspectives of sustainable development and environmental performance with regards to three main issues: energy, water and carbon emissions, whereas the later part highlights the importance of solar technology as a low-polluted alternative to fossil fuels in the retail sector. Sustainable development of energy, water and environmental precautions such as reducing carbon emissions are of interest to wider branches of industries including retail, energy and water sector, governmental policymakers, researchers, educators and society. The purpose of this chapter is to increase the debate of the key issues of sustainable development regarding environment, energy and water in the modern times.

Joon Y. Park is one of the pioneers in developing nonlinear cointegrating regression. Since his initial work with Phillips (Park & Phillips, 2001) in the area, the past two decades have witnessed a surge of interest in modeling nonlinear nonstationarity in macroeconomic and financial time series, including parametric, nonparametric and semiparametric specifications of such models. These developments have provided a framework of econometric estimation and inference for a wide class of nonlinear, nonstationary relationships. In honor of Joon Y. Park, this chapter contributes to this area by exploring nonparametric estimation of functional-coefficient cointegrating regression models where the structural equation errors are serially dependent and the regressor is endogenous. The self-normalized local kernel and local linear estimators are shown to be asymptotic normal and to be pivotal upon an estimation of co-variances. Our new results improve those of Cai et al. (2009) and open up inference by conventional nonparametric method to a wide class of potentially nonlinear cointegrated relations.

This chapter studies banks’ loan pricing behavior in mainland China during 2003–2013 by applying panel regressions to firm-level loan data and the estimated default likelihood for listed companies. The authors find that with the progress of market-oriented financial reforms, banks generally require compensation for their exposure to borrowers’ default risks. It is even more so if the borrower is a non-state-owned enterprise (non-SOE), mainly due to the pricing behavior of the Big Four banks. Bank lending rates are shown to be less sensitive to the default risks of state-owned enterprises (SOEs). Our results also reveal that banks priced in firm default risks before 2008 financial crisis, but not necessarily so after the crisis. As for industries, we find that after the 2008 Global Financial Crisis, the real estate sector and other government-supported industries tended to enjoy better terms on loan pricing in terms of default risks. We believe the main reason is that the government stimulus policies tilted toward those industries that have played crucial roles in China’s economic growth.

Advances in artificial intelligence have ushered in new opportunities for marketers in the domain of content generation. We discuss approaches that have emerged to generate text and image content. Drawing on the customer equity framework, we then discuss the potential applications of automated content generation for customer acquisition, relationship development, and customer retention. We conclude by discussing important considerations that businesses must make prior to adopting automated content generation.

Sustainability has become the topic of the era, and the world is moving toward a circular economy. As part of it, companies are also integrating sustainability in supply chain management. Therefore, this study conducts a technology-empowered systematic literature review to examine the state of the literature on the technologies in the sustainable supply chain. The review reveals that limited studies have been conducted in the area. There is still scope for the researchers to explore new innovative technologies, many clusters in author collaboration, and involve more sectors in the study. Future studies can enrich the field by employing other methodologies like meta-analysis review.

In this paper, we study a partially linear dynamic panel data model with fixed effects, where either exogenous or endogenous variables or both enter the linear part, and the lagged-dependent variable together with some other exogenous variables enter the nonparametric part. Two types of estimation methods are proposed for the first-differenced model. One is composed of a semiparametric GMM estimator for the finite-dimensional parameter θ and a local polynomial estimator for the infinite-dimensional parameter m based on the empirical solutions to Fredholm integral equations of the second kind, and the other is a sieve IV estimate of the parametric and nonparametric components jointly. We study the asymptotic properties for these two types of estimates when the number of individuals N tends to ∞ and the time period T is fixed. We also propose a specification test for the linearity of the nonparametric component based on a weighted square distance between the parametric estimate under the linear restriction and the semiparametric estimate under the alternative. Monte Carlo simulations suggest that the proposed estimators and tests perform well in finite samples. We apply the model to study the relationship between intellectual property right (IPR) protection and economic growth, and find that IPR has a non-linear positive effect on the economic growth rate.

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.