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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.

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.

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.

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.

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.

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 demonstrates that fixed-effects and first-differences models often understate the effect of interest because of the variation used to identify the model. In particular, the within-unit time-series variation often reflects transitory fluctuations that have little effect on behavioral outcomes. The data in effect suffer from measurement error, as a portion of the variation in the independent variable has no effect on the dependent variable. Two empirical examples are presented: one on the relationship between AFDC and fertility and the other on the relationship between local economic conditions and AFDC expenditures.

This paper aims to test the significance of unobservable firm-specific effects on a capital structure model.

The paper employs the restricted least squares method to test the significance of unobservable firm-specific effects in a fixed effects model that includes unobservable effects against a pooled ordinary least squares model that excludes unobservable effects.

The empirical findings indicate that models that include unobservable firm-specific effects are correctly specified.

The limitation of this study comes from lack of data to measure unobservable effects such as managerial ability or managerial skills. Future research can develop index measures of managerial ability or managerial skills and borrow from management theory to explain the connection between managerial ability or managerial skills and firms' capital structure.

The findings imply that a capital structure model that excludes firm-specific effects could be mis-specified because such a model does not control for unobservable firm-specific factors such as managerial ability or managerial skills which have significant effects on firms' capital structure decisions.

The findings are important because the paper applies the restricted least squares method to test the significance of unobservable firm-specific effects. This technique has not been applied previously. The paper contributes to capital structure research in the fast growing South Africa.

This chapter investigates which factors contribute to (small) shareholder attendance using a hand-collected panel data set with information about turnout rates, voting behaviour and ownership structures of companies that are listed in seven Member States. We document how ownership concentration positively affects total shareholder turnout, but has a negative effect on small shareholder turnout. Voting power also affects small shareholder turnout rates; the greater small shareholder voting power, the greater their eagerness to vote. In addition, total and small shareholder turnout is higher the more important the meeting agenda. And, small shareholders tend to free-ride on large institutional shareholders and corporate insiders, but the magnitude of the free-rider effect is larger for the latter category of blockholders. Our results provide some important insights for the debate on shareholder rights and the role of the AGM in corporate governance. The results show that, despite the criticism, the AGM still plays an important role in small shareholder monitoring. Some topics seem to clearly motivate small shareholders to attend, while others are less relevant. Policy makers can stimulate shareholder monitoring by focusing on the factors that are determined in this study, but it is important to consider possible endogeneity issues as well.

This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors span Rn (long panels), and the sample exogenous vectors span a proper subspace of Rⁿ (short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.