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In this paper, we consider the estimation of common breaks for linear panel data models by means of screening and ranking algorithm. For static and dynamic panel data models, we estimate the regression coefficients using covariance estimation and generalized method of moments, respectively, and apply a screening and ranking algorithm on this basis. The possible break points are first screened by constructing local statistics based on the coefficient estimators, then further screened by the thresholding rule, and finally the final break points are screened by the information criterion. Monte Carlo simulations demonstrate that the proposed methods work well in finite samples. We apply the screening and ranking algorithm to study the influence of rural residents’ consumption demand on China’s economic growth using a panel of 31 provinces from 2005 to 2023 and find a break point in the model.

This study presents an analysis of the relationship between per capita CO2 emissions as an environmental degradation indicator and per capita gross domestic product (GDP) as an economic growth indicator within the framework of the Environmental Kuznets Curve (EKC). For this purpose, non-linear panel models are estimated for the Annex I countries, non-Annex countries, and whole parties with respect to data availability of the United States Convention on Climate Change (UNFCCC) for the period 1960–2012. The empirical results of the panel smooth transition models (PSTR) show that the environmental deterioration rises in the first phase of growth for all data sets. Afterwards, the environmental degradation cannot be prevented, but the increase in the amount of environmental degradation decreases. The findings of this study give an insight regarding the differential environmental impact of economic growth between developed and developing countries. While the validity of a traditional EKC relation regarding the CO2 emissions cannot be affirmed for any group of countries in our sample, empirical results indicate the existence of multiple regimes where economic growth hampers environmental quality, but its severity decreases at each consecutive regime.

A central question in class action litigation is all or nearly all proposed class members were injured by the alleged conduct, that is, does “common impact” exist. In this paper, we demonstrate that a two‐step econometric methodology can be used to estimate customer‐level overcharges, providing a basis for evaluating whether common impact exists. The estimates obtained from this methodology possess desirable properties. We also address several common misunderstandings and misinterpretations of the two‐step approach in practice.

The COVID-19 pandemic has recently caused the loss of millions of lives, and billions of others have been deeply affected. This crisis has changed the way people live, think about life, and perceive happiness. The aim of this study is to reveal differences between geographical regions by investigating the effect of the happiness variable on different countries during the international COVID-19 pandemic. The primary purpose is to demonstrate how such a pandemic may affect different countries in terms of happiness at the individual level and to identify possible strategies for the future. With this aim, both static and dynamic panel data models were used while applying fixed effects, random effects, and the generalized method of moments (GMM). A basic assumption in panel data models is that the coefficients do not change over time. This assumption is unlikely to hold, however, especially during major devastating events like COVID-19. Therefore, the piecewise linear panel data model was applied in this study. As a result of empirical analysis, pre- and post-COVID differences were seen between different geographical regions. Based on analysis conducted for three distinct geographical regions with piecewise linear models, it was determined that the piecewise random effects model was appropriate for European and Central Asian countries, the piecewise FGLS model for Latin American and Caribbean countries, and the piecewise linear GMM model for South Asian countries. According to the results, there are many variables that affect happiness, which vary according to different geographical conditions and societies with different cultural values.

To achieve the coveted objectives of sustainable development, the Bangladesh government has devised a comprehensive strategy to promote the adoption of innovative agricultural practices capable of addressing the critical challenges at the intersection of food, energy, water, and ecosystems (FEWE). This plan prioritises the increased uptake of solar irrigation and recommended fertiliser application (SIRFA) technologies to enhance sustainable food production while effectively managing energy and water resources, and fostering ecological balance. Thus, this study analysed seven years of panel data (2015–2021) to assess the long-term impact of SIRFA technology adoption on production costs (PC) and return on investment (ROI) among Bangladeshi farmers cultivating the BRRI-dhan29 rice variety in the water-scarce, acidic soils of Dinajpur. Utilising the generalised estimating equation (GEE) with a population-averaged model, we investigated the determinants of adoption. Additionally, we applied a two-stage residual inclusion (2SRI) method alongside six linear panel-data models to analyse the impact of SIRFA adoption. Our findings revealed that adopters experienced reduced production costs and enhanced ROI through SIRFA technology adoption. These results emphasised the urgent need for region-specific policy interventions to facilitate the broader adoption of SIRFA technologies.

Spatio-temporal data analysis is an emerging research area due to the development and application of novel computational techniques allowing for the analysis of large spatio-temporal databases. We consider a general class of spatio-temporal linear models, where the number of structural breaks can tend to infinity. A procedure for simultaneously detecting all the change points is developed rigorously via the construction of adaptive group lasso penalty. Consistency of the multiple change point estimation is established under mild technical conditions even when the true number of change points sn diverges with the series length n. The adaptive group lasso can be substituted by the group lasso and other non-convex group selection penalty functions such as group SCAD or group MCP. The simulation studies demonstrate that our procedure is stable and accurate. Two empirical examples from property market, including the housing transaction price in Baton Rouge and the commodity apartment price in Hong Kong, are analyzed to fully illustrate the proposed methodology.

The problem of predicting profitability is exceptionally relevant for investors and company owners. This paper examines the factors affecting firm performance and tests and compares various methods based on linear and non-linear dependencies between variables for predicting firm performance. In this study, the methods include random effects regression, individual machine learning algorithms with optimizers (DNN, LSTM, and Random Forest), and advanced machine learning methods consisting of sets of algorithms (portfolios and ensembles). The training sample includes 551 retail-oriented companies and data for 2017–2019 (panel data, 1653 observations). The test sample contains data for these companies for 2020. This study combines two approaches (stages): an econometric analysis of the influence of factors on the company’s profitability and machine learning methods to predict the company’s profitability. To compare forecasting methods, we used parametric and non-parametric predictive measures and ANOVA. The paper shows that previous profitability has a strong positive impact on a firm’s performance. We also find a non-linear positive effect of sales growth and web traffic on firm profitability. These variables significantly improve the prediction accuracy. Regression is inferior in forecast accuracy to machine learning methods. Advanced methods (portfolios and ensembles) demonstrate better and more steady results compared with individual machine learning methods.

Nous modélisons la participation au marché du travail des femmes qui vivent en couple. Nous estimons sur une période récente un modèle dynamique avec effets aléatoires à partir des données de l’ECHP et de l’EU-SILC. Nous proposons un estimateur des paramètres d’un tel modèle non linéaire en présence de variables explicatives endogènes comme c’est le cas pour la fécondité. Il s’agit d’un estimateur des variables instrumentales. Les résultats montrent une persistance importante du comportement des femmes sur le marché du travail. La présence d’un enfant jeune à un effet important sur la participation. Ces résultats indiquent qu’il est difficile pour les femmes de concilier une maternité et une vie professionnelle active. We model participation to the labor market of women who live in couple in France. We estimate for two periods of time going from 1994 to 2001 and from 2004 to 2013 a dynamic model with random effects using the ECHP (European community Household Panel) and the EU-SILC (European Union Statistics on Income and Living Conditions). We propose then an estimator in order to estimate the parameters of such a non linear model and in presence of endogenous explanatory variables such as fertility. The IV estimator is used to estimate the labour market participation of women. The results we obtain show a high persistence of the labor market behavior of women. The presence of a young children has a large and significant impact on the participation status. These results are indicating that it is difficult or costly for women to conciliate maternity and an active working life.

Space-time panel data widely exist in many research fields such as economics, management, geography and environmental science. It is of interest to study the relationship between response variable and regressors which come from above fields by establishing regression models. This paper introduces a new fixed effects partially linear varying coefficient panel data regression model with nonseparable space-time filters. On the basis of approximating the varying coefficient functions with a powerful B-spline method, the profile quasi-maximum likelihood estimators of parameters and varying coefficient functions are constructed. Under some regular conditions, we derive their consistency and asymptotic normality. Monte Carlo simulation shows that our estimates have good finite performance and ignoring spatial and serial correlations may lead to inefficiency of estimates. Finally, the driving forces of Chinese resident consumption rate are studied using our estimation method.