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This chapter proposes an approach toward the estimation of cross-sectional sample selection models, where the shocks on the units of observation feature some interdependence through spatial or network autocorrelation. In particular, this chapter improves on prior Bayesian work on this subject by proposing a modified approach toward sampling the multivariate-truncated, cross-sectionally dependent latent variable of the selection equation. This chapter outlines the model and implementation approach and provides simulation results documenting the better performance of the proposed approach relative to existing ones.

We put forward the idea that for model selection the intrinsic priors are becoming a center of a cluster of a dominant group of methodologies for objective Bayesian Model Selection.

The intrinsic method and its applications have been developed in the last two decades, and has stimulated closely related methods. The intrinsic methodology can be thought of as the long searched approach for objective Bayesian model selection and hypothesis testing.

In this paper we review the foundations of the intrinsic priors, their general properties, and some of their applications.

We extend Vuong’s (1989) model-selection statistic to allow for complex survey samples. As a further extension, we use an M-estimation setting so that the tests apply to general estimation problems – such as linear and nonlinear least squares, Poisson regression and fractional response models, to name just a few – and not only to maximum likelihood settings. With stratified sampling, we show how the difference in objective functions should be weighted in order to obtain a suitable test statistic. Interestingly, the weights are needed in computing the model-selection statistic even in cases where stratification is appropriately exogenous, in which case the usual unweighted estimators for the parameters are consistent. With cluster samples and panel data, we show how to combine the weighted objective function with a cluster-robust variance estimator in order to expand the scope of the model-selection tests. A small simulation study shows that the weighted test is promising.

We consider the Bayes estimation of a multivariate sample selection model with p pairs of selection and outcome variables. Each of the variables may be discrete or continuous with a parametric marginal distribution, and their dependence structure is modeled through a Gaussian copula function. Markov chain Monte Carlo methods are used to simulate from the posterior distribution of interest. The methods are illustrated in a simulation study and an application from transportation economics.

The purpose of this study is to examine the effects of health on wages of Australian workers, with a focus on gender differences and the role of macroeconomic conditions in the effects.

The first 15 waves of the Household, Income and Labour Dynamics in Australia survey are used to estimate a wage model that accounts for the endogeneity of health, unobserved heterogeneity and sample selection bias.

The results show that, after accounting for the endogeneity of health, unobserved heterogeneity and sample selection bias, better health increases wages for Australian male workers, but not for female workers. The results also show that accounting for the endogeneity of health, unobserved heterogeneity and potential sample selection bias is important in estimating the effects of health on wages. In particular, a simple ordinary least squares estimator would underestimate the effect of health on wages for males, while overestimate it for females, and simply addressing the endogeneity of health using instrumental variables could overestimate the effect for both genders. It is also found that the effects of health on wages fall under depressed macroeconomic conditions, perhaps due to reduced job mobility and increased presentism during a recession.

This study adds to the international literature on the effects of health on wages by providing empirical evidence from Australia. The model applied to estimate the effects takes advantage of a panel dataset to address the bias resulting potentially from all the sources of the endogeneity of health, unobserved heterogeneity and sample selection. The results indeed show that failing to address these issues would substantially bias the estimated effects of health on wages.

Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.

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.

Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.

A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.

The study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.

This research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.

This paper briefly introduces the concept of model selection, reviews recent development in the area of econometric analysis of model selection and addresses some of the crucial issues that are being faced by researchers in their routine research problems. The paper emphasizes on the importance of model selection, particularly the information criteria and penalty functions based model selection procedures which are useful for economists and finance researchers.

Farmer organizations (FOs) can elevate many of the production- and marketing-related challenges by ensuring access to technology, farming information and loan for inputs and mechanization. This study assesses the major factors that affect the participation in FOs by the smallholder rice farmers in Bangladesh and evaluates the impacts of the participation on rice yield and production efficiency.

The present study used primary data collected from 1,000 smallholder rice farmers in northwest Bangladesh, consisting of 250 farmers those participated in an organization. This study utilized a sample selection stochastic production frontier (SPF) method, a combination of the conventional SPF and the Heckman's sample selection model, to control for biases stemming from observed and unobserved attributes.

This study demonstrates that participation in an organization is significantly affected by smallholder rice farmers' education, occupation, family size, household income, land ownership and the location where they reside. At the same time, the participation status affects the productivity of smallholder farmers. Findings indicate that farmers who participated in an organization had higher rice yield (11% more) and were technically more efficient (1.4 percentage points higher) compared to farmers who did not participate.

Since this study was carried out with representative sampled farmers from northwest Bangladesh, the findings may not represent all farmers' perceptions of FOs in the country.

Even though more than 200,000 FOs are currently in operation, knowledge regarding the effectiveness of Bangladesh's FOs is limited. Notably, this study used a relatively new method, sample selection SPF model, to investigate the impact of FOs on the production efficiency of smallholder rice farmers in northwest Bangladesh. Therefore, this study contributes to the literature in elucidating the factors affecting participation in FOs and its impact on rice yield and efficiency. Since FOs have been somewhat ineffective in their role as service providers in Bangladesh, this study’s results will guide policymakers to intervene more successfully regarding the changes needed.