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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 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.

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (2002) (CS) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.

The purpose of the chapter is to test the hypothesis that food safety (chemical) standards act as barriers to international seafood imports. We use zero-accounting gravity models to test the hypothesis that food safety (chemical) standards act as barriers to international seafood imports. The chemical standards on which we focus include chloramphenicol required performance limit, oxytetracycline maximum residue limit, fluoro-quinolones maximum residue limit, and dichlorodiphenyltrichloroethane (DDT) pesticide residue limit. The study focuses on the three most important seafood markets: the European Union’s 15 members, Japan, and North America.Our empirical results confirm the hypothesis and are robust to the OLS as well as alternative zero-accounting gravity models such as the Heckman estimation and the Poisson family regressions. For the choice of the best model specification to account for zero trade and heteroskedastic issues, it is inconclusive to base on formal statistical tests; however, the Heckman sample selection and zero-inflated negative binomial (ZINB) models provide the most reliable parameter estimates based on the statistical tests, magnitude of coefficients, economic implications, and the literature findings. Our findings suggest that continually tightening of seafood safety standards has had a negative impact on exporting countries. Increasing the stringency of regulations by reducing analytical limits or maximum residue limits in seafood in developed countries has negative impacts on their bilateral seafood imports. The chapter furthers the literature on food safety standards on international trade. We show competing gravity model specifications and provide additional evidence that no one gravity model is superior.

This chapter analyzes the empirical relationship between the pricesetting/consumption behavior and the sources of persistence in inflation and output. First, a small-scale New-Keynesian model (NKM) is examined using the method of moment and maximum likelihood estimators with US data from 1960 to 2007. Then a formal test is used to compare the fit of two competing specifications in the New-Keynesian Phillips Curve (NKPC) and the IS equation, that is, backward- and forward-looking behavior. Accordingly, the inclusion of a lagged term in the NKPC and the IS equation improves the fit of the model while offsetting the influence of inherited and extrinsic persistence; it is shown that intrinsic persistence plays a major role in approximating inflation and output dynamics for the Great Inflation period. However, the null hypothesis cannot be rejected at the 5% level for the Great Moderation period, that is, the NKM with purely forward-looking behavior and its hybrid variant are equivalent. Monte Carlo experiments investigate the validity of chosen moment conditions and the finite sample properties of the chosen estimation methods. Finally, the empirical performance of the formal test is discussed along the lines of the Akaike's and the Bayesian information criterion.

To determine the role of status information conveyance in a negative reward allocation setting.

Using previously published experimental data, we test the relative effects of status information conveyed by expressive and indicative status cues on the allocation of a negative reward. Further, we construct an alternative graph theoretic model of expectation advantage which is also tested to determine its model fit relative to the classic model of Reward Expectations Theory.

Results provide strong support for the conclusion that status information conveyed by expressive status cues influences reward allocations more than information conveyed by indicative cues. We also find evidence that our alternative graph theoretic model of expectation advantage improves model fit.

This research is the first to test the relative impact of expressive versus indicative status cues on the allocation of negative rewards and shows that status characteristics can have differential impacts on these allocations contingent on how characteristics are conveyed. Furthermore, the research suggests a graph theoretic model that allows for this differentiation based on information conveyance and provides empirical support for its structure in a negative reward allocation environment.

Future research is required to validate the results in positive reward situations.

The results show that an individual’s expectations are altered by varying the manner in which status information is presented, thereby influencing the construction and maintenance of status hierarchies and the inequalities those structures generate. Thus, this research has implications for any group or evaluative task where status processes are relevant.

From a theoretical point of view, a spatial econometric model can contain both a spatially lagged dependent variable (spatial lag) and a spatially autocorrelated error term (spatial error). However, such models are rarely used in practice. This is because (assuming a lattice model approach is used for both the spatial lag and spatial error) the model is difficult to estimate¹ unless the weight matrices are different for the spatial lag and the spatial error.