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This chapter revisits the Hausman (1978) test for panel data. It emphasizes that it is a general specification test and that rejection of the null signals misspecification and is not an endorsement of the fixed effects estimator as is done in practice. Non-rejection of the null provides support for the random effects estimator which is efficient under the null. The chapter offers practical tips on what to do in case the null is rejected including checking for endogeneity of the regressors, misspecified dynamics, and applying a nonparametric Hausman test, see Amini, Delgado, Henderson, and Parmeter (2012, chapter 16). Alternatively, for the fixed effects die hard, the chapter suggests testing the fixed effects restrictions before adopting this estimator. The chapter also recommends a pretest estimator that is based on an additional Hausman test based on the difference between the Hausman and Taylor estimator and the fixed effects estimator.

This paper uses a sample of school age children from the Nepal Demographic Health Survey (NDHS) to examine the relationship between maternal education and child schooling in Nepal. Taking advantage of the two-stage stratified sample design, we estimate a sample selection model controlling for cluster fixed effects. These results are then compared to OLS and Tobit models. Our analysis shows that being male significantly increases the likelihood of attending school and for those children attending school, it also affects the years of schooling. Parental education has a similarly positive effect on child school, but interestingly we find maternal education having a relatively greater effect on the schooling of girls. Our results also point to household wealth as having a positive effect on both the probability of schooling and the years of schooling in all our models, with the magnitude of these effects being similar for male and female children. Finally, a comparison of our results with a model ignoring cluster fixed effects produces results that are statistically different both in signs and in the levels of significance.

The purpose of this paper is to explore the relationship between the ratification of the United Nations’ (UN’s) Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) and women’s and girls’ health outcomes using a unique longitudinal data set of 192 UN-member countries that encompasses the years from 1980 to 2011.

The authors focus on the impact of CEDAW ratification, number of reports submitted after ratification, years passed since ratification, and the dynamic impact of CEDAW ratification by utilizing ordinary least squares (OLS) and panel fixed effects methods. The study investigates the following women’s and girls’ health outcomes: total fertility rate, adolescent fertility rate, infant mortality rate, maternal mortality ratio, neonatal mortality rate, female life expectancy at birth (FLEB), and female to male life expectancy at birth.

The OLS and panel country and year fixed effects models provide evidence that the impact of CEDAW ratification on women’s and girls’ health outcomes varies by global regions. While the authors find no significant gains in health outcomes in European and North-American countries, the countries in the Northern Africa, sub-Saharan Africa, Southern Africa, Caribbean and Central America, South America, Middle-East, Eastern Asia, and Oceania regions experienced the biggest gains from CEDAW ratification, exhibiting reductions in total fertility, adolescent fertility, infant mortality, maternal mortality, and neonatal mortality while also showing improvements in FLEB. The results provide evidence that both early commitment to CEDAW as measured by the total number of years of engagement after the UN’s 1980 ratification and the timely submission of mandatory CEDAW reports have positive impacts on women’ and girls’ health outcomes. Several sensitivity tests confirm the robustness of main findings.

This study is the first comprehensive attempt to explore the multifaceted relationships between CEDAW ratification and female health outcomes. The study significantly expands on the methods of earlier research and presents novel methods and findings on the relationship between CEDAW ratification and women’s health outcomes. The findings suggest that the impact of CEDAW ratification significantly depends on the country’s region. Furthermore, stronger engagement with CEDAW (as indicated by the total number of years following country ratification) and the submission of the required CEDAW reports (as outlined in the Convention’s guidelines) have positive impacts on women’s and girls’ health outcomes.

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.

Hausman (1978) represented a tectonic shift in inference related to the specification of econometric models. The seminal insight that one could compare two models which were both consistent under the null spawned a test which was both simple and powerful. The so-called ‘Hausman test’ has been applied and extended theoretically in a variety of econometric domains. This paper discusses the basic Hausman test and its development within econometric panel data settings since its publication. We focus on the construction of the Hausman test in a variety of panel data settings, and in particular, the recent adaptation of the Hausman test to semiparametric and nonparametric panel data models. We present simulation experiments which show the value of the Hausman test in a nonparametric setting, focusing primarily on the consequences of parametric model misspecification for the Hausman test procedure. A formal application of the Hausman test is also given focusing on testing between fixed and random effects within a panel data model of gasoline demand.

One way to control for the heterogeneity in panel data is to allow for time-invariant, individual specific parameters. This fixed effect approach introduces many parameters into the model which causes the “incidental parameter problem”: the maximum likelihood estimator is in general inconsistent. Woutersen (2001) shows how to approximately separate the parameters of interest from the fixed effects using a reparametrization. He then shows how a Bayesian method gives a general solution to the incidental parameter for correctly specified models. This paper extends Woutersen (2001) to misspecified models. Following White (1982), we assume that the expectation of the score of the integrated likelihood is zero at the true values of the parameters. We then derive the conditions under which a Bayesian estimator converges at rate N where N is the number of individuals. Under these conditions, we show that the variance-covariance matrix of the Bayesian estimator has the form of White (1982). We illustrate our approach by the dynamic linear model with fixed effects and a duration model with fixed effects.

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.

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

Purpose: The present study investigates how the performance of Greek bank branching varies when the external environment causes dramatic changes that are reflected in recession and capital control effects.

Design/Methodology: A unique dataset of accounting Profit and Loss statements of retail branches of a systemic Greek commercial bank, closely supervised by the European Central Bank (ECB), is utilized. A profit bootstrap Data Envelopment Analysis (DEA) model is selected to measure the bank branch efficiency. The derived efficiency estimates are analyzed through a second-stage panel data regression analysis against a set of efficiency drivers related to branch profitability, diversification of income, branch size, and branch activity.

Findings: The results indicate that recession negatively affects branch efficiency in the short and long run. The occurrence of recession significantly intensifies the efficiency premium of branch profitability, reduces the efficiency premium of diversification of income (i.e., a negative efficiency effect is recorded during the early recession period), while mitigating the generally negative efficiency effect of branch size. The analysis of efficiency effects from the deep recession period that encompasses capital controls reveals the importance of diversification of income for the improvement of profit efficiency at bank branch level.

Originality/Value: This is the first branch banking study that explores branch efficiency alteration and the dynamic of branch efficiency drivers when the economy suddenly enters recession and afterwards when conditions are becoming extremely difficult and consequently capital controls are imposed on the economy.

The New Zealand (NZ) dairy industry faces the challenge of increasing productivity and dealing with public concerns over nutrient pollution. The effective policy needs to address regional differences in productivity and fertilizer use. The purpose of this paper is to investigate how spatial effects influence the relationship between dairy yields and intensive farming practices across regions in NZ.

This paper employs spatial panel data models to establish whether unobserved spatial effects exist in the relationship between dairy yields and nutrient inputs regionally and nationally using 2002, 2007 and 2012 data from Statistics NZ and DairyNZ.

The results show positive spatial spillovers for most intensive inputs. The high level of effluent use and estimated negative yield response to nitrogen suggests that an opportunity exists for greater use of effluent as a substitute for nitrogenous fertilizer. Substitution has the potential to reduce dependence on fertilizer and contribute to a reduction in the nutrient pollution.

This paper is the first empirical application of spatial econometric methods to examine the spatial relevance of dairy yields and intensive farming in NZ. In particular, the spatial panel data model accounts for cross-sectional dependence and controls for heterogeneity. The results contribute to an understanding of how farmers can improve their management of intensive inputs and contribute to the formation of regional environmental policy that recognizes regional heterogeneity.