Rational behaviour: A comparison between the theory stemming from de Finetti's work and some other leading theories

We present some interesting features of de Finetti's decision theory; then we extended the theory. The extended theory has a normative character and is of the expected utility kind, but it is also very adaptable. By comparing it with some leading theories, we find that our theory is compatible with consideration of the whole probability distribution — it can even accommodate Allais' paradox -, while it is not generally compatible with probability weighting. We are mainly interested in the normative point of view.     

Random Effects Estimators with many Instrumental Variables

In this paper we propose a new estimator for a model with one endogenous regressor and many instrumental variables. Our motivation comes from the recent literature on the poor properties of standard instrumental variables estimators when the instrumental variables are weakly correlated with the endogenous regressor. Our proposed estimator puts a random coefficients structure on the relation between the endogenous regressor and the instruments. The variance of the random coefficients is modelled as an unknown parameter. In addition to proposing a new estimator, our analysis yields new insights into the properties of the standard two‐stage least squares (TSLS) and limited‐information maximum likelihood (LIML) estimators in the case with many weak instruments. We show that in some interesting cases, TSLS and LIML can be approximated by maximizing the random effects likelihood subject to particular constraints. We show that statistics based on comparisons of the unconstrained estimates of these parameters to the implicit TSLS and LIML restrictions can be used to identify settings when standard large sample approximations to the distributions of TSLS and LIML are likely to perform poorly. We also show that with many weak instruments, LIML confidence intervals are likely to have under‐coverage, even though its finite sample distribution is approximately centered at the true value of the parameter. In an application with real data and simulations around this data set, the proposed estimator performs markedly better than TSLS and LIML, both in terms of coverage rate and in terms of risk.     

Confidence Intervals for Partially Identified Parameters

Recently a growing body of research has studied inference in settings where parameters of interest are partially identified. In many cases the parameter is real‐valued and the identification region is an interval whose lower and upper bounds may be estimated from sample data. For this case confidence intervals (CIs) have been proposed that cover the entire identification region with fixed probability. Here, we introduce a conceptually different type of confidence interval. Rather than cover the entire identification region with fixed probability, we propose CIs that asymptotically cover the true value of the parameter with this probability. However, the exact coverage probabilities of the simplest version of our new CIs do not converge to their nominal values uniformly across different values for the width of the identification region. To avoid the problems associated with this, we modify the proposed CI to ensure that its exact coverage probabilities do converge uniformly to their nominal values. We motivate this modified CI through exact results for the Gaussian case.     

A local-influence-based diagnostic approach to a speeded item response theory model

Summary.  An item response theory model for dealing with omitted responses in a test is proposed. In this model formulation, non-response does not only depend on an examinee's ability and on item difficulty, but additionally also on 'test speededness'. Using a local-influence-based diagnostic approach, the sensitivity of the model regarding assumptions concerning the drop-out mechanism is explored. The methodology proposed is applied to the Chilean Sistema de Medición de la Calidad de la Educación mathematics test case-study.     

Evaluation of the run-length distribution for a combined Shewhart-EWMA control chart

A simple algorithm is introduced for computing the run length distribution of a monitoring scheme combining a Shewhart chart with an Exponentially Weighted Moving Average control chart. The algorithm is based on the numerical approximation of the integral equations and integral recurrence relations related to the run-length distribution. In particular, a Clenshaw-Curtis product-integration rule is applied for handling discontinuities in the integrand function due to the simultaneous use of the two control schemes. The proposed algorithm, implemented in R and publicy available, compares favourably with the Markov chain approach originally used to approximate the run length properties of the combined Shewhart-EWMA.     

Do smart parents raise smart children? The intergenerational transmission of cognitive abilities

Complementing prior research on income and educational mobility, we examine the intergenerational transmission of cognitive abilities. We find that individuals’ cognitive skills are positively related to their parents’ abilities, despite controlling for educational attainment and family background. Differentiating between mothers’ and fathers’ IQ transmission, we find different effects on the cognition of sons and daughters. Cognitive skills that are based on past learning are more strongly transmitted between generations than skills that are related to innate abilities. Our findings are not compatible with a pure genetic model but rather point to the importance of parental investments for children’s cognitive outcomes.