Synthetic learner: model-free inference on treatments over time

Understanding of the effect of a particular treatment or a policy pertains to many areas of interest -- ranging from political economics, marketing to health-care and personalized treatment studies. In this paper, we develop a non-parametric, model-free test for detecting the effects of treatment over time that extends widely used Synthetic Control tests. The test is built on counterfactual predictions arising from many learning algorithms. In the Neyman-Rubin potential outcome framework with possible carry-over effects, we show that the proposed test is asymptotically consistent for stationary, beta mixing processes. We do not assume that class of learners captures the correct model necessarily. We also discuss estimates of the average treatment effect, and we provide regret bounds on the predictive performance. To the best of our knowledge, this is the first set of results that allow for example any Random Forest to be useful for provably valid statistical inference in the Synthetic Control setting. In experiments, we show that our Synthetic Learner is substantially more powerful than classical methods based on Synthetic Control or Difference-in-Differences, especially in the presence of non-linear outcome models

Optimal Estimation Methodologies for Panel Data Regression Models

This survey study discusses main aspects to optimal estimation methodologies for panel data regression models. In particular, we present current methodological developments for modeling stationary panel data as well as robust methods for estimation and inference in nonstationary panel data regression models. Some applications from the network econometrics and high dimensional statistics literature are also discussed within a stationary time series environment

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Sustainability and the Environmental Kuznets Curve Conjecture

This book focuses on empirical analyses of the Environmental Kuznets Curve. Most papers apply their research on CO2 emissions. The papers in this Special Issue seek to improve modeling techniques to prevent econometrical flaws (e.g., by adding additional explanatory variables and/or moving away from linear regression models) and apply their models to specific countries or groups of countries

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Does renewable energy consumption reduce ecological footprint? Evidence from eight developing countries of Asia

Economic and demographic transitions led by the persistent increase in the per capita income have challenged the environmental conservation drive in most of the developing nations. Therefore, in recent years, policymakers emphasized the need for navigating the harmful impacts of economic growth endeavors on the established ecosystem. In this regard, the widespread usage of renewable energy solutions has helped in restoring the environmental quality in both developed and developing countries. Keeping this in mind, in the present study, we examined the long run and short-run impacts of per capita income, renewable energy, life expectancy, and population density on the ecological footprint in the eight developing countries of South and Southeast Asia from 1990-2015. In the selected nations, these variables appear to be the potential drivers of the ecological footprint. To calculate the common coefficients, we have employed the cross-sectional augmented autoregressive distributed lag (CS-ARDL) approach, as this approach handles the cross-sectional dependency issue efficiently and provides the short-run and long-run coefficients. The long-run results supported the need for low pollution-intense energy resources because the association between per capita income and ecological footprint is found N-shaped. Further, the study established that the increased use of renewable energy has significantly reduced the ecological footprint in the region. However, the increased population density has led to an increase in pollution emissions in these countries. Similarly, the impact of life expectancy on the ecological footprint is found positive but insignificant. Based on the findings, a multipronged policy framework has been designed, so that these nations can attain the objectives of certain sustainable development goals (SDGs)

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio